bluuebunny/crossref_metadata_2025 huggingface dataset:
A Robust Chinese Named Entity Recognition Method Based on Integrating Dual-Layer Features and CSBERT
Authors: Xu Yingjie , Tan Xiaobo , Tong Xin , Zhang Wenbo
Published: January 2024

In the rapidly evolving field of cybersecurity, the integration of multi-source, heterogeneous, and fragmented data into a coherent knowledge graph has garnered considerable attention. Such a graph elucidates semantic interconnections, thereby facilitating sophisticated analytical decision support. Central to the construction of a cybersecurity knowledge graph is Named Entity Recognition (NER), a critical technology that converts unstructured text into structured data. The efficacy of NER is pivotal, as it directly influences the integrity of the knowledge graph. The task of NER in cybersecurity, particularly within the Chinese linguistic context, presents distinct challenges. Chinese text lacks explicit space delimiters and features complex contextual dependencies, exacerbating the difficulty in discerning and categorizing named entities. These linguistic characteristics contribute to errors in word segmentation and semantic ambiguities, impeding NER accuracy. This paper introduces a novel NER methodology tailored for the Chinese cybersecurity corpus, termed CSBERT-IDCNN-BiLSTM-CRF. This approach harnesses Iterative Dilated Convolutional Neural Networks (IDCNN) for extracting local features, and Bi-directional Long Short-Term Memory networks (BiLSTM) for contextual understanding. It incorporates CSBERT, a pre-trained model adept at processing few-shot data, to derive input feature representations. The process culminates with Conditional Random Fields (CRF) for precise sequence labeling. To compensate for the scarcity of publicly accessible Chinese cybersecurity datasets, this paper synthesizes a bespoke dataset, authenticated by data from the China National Vulnerability Database, processed via the YEDDA annotation tool. Empirical analysis affirms that the proposed CSBERT-IDCNN-BiLSTM-CRF model surpasses existing Chinese NER frameworks, with an F1-score of 87.30% and a precision rate of 85.89%. This marks a significant advancement in the accurate identification of cybersecurity entities in Chinese text, reflecting the model’s robust capability to address the unique challenges presented by the language’s structural intricacies.
A Multi-Scale Video Longformer Network for Action Recognition
Authors: Chen Congping , Zhang Chunsheng , Dong Xin
Published: January 2024

Action recognition has found extensive applications in fields such as video classification and security monitoring. However, existing action recognition methods, such as those based on 3D convolutional neural networks, often struggle to capture comprehensive global information. Meanwhile, transformer-based approaches face challenges associated with excessively high computational complexity. We introduce a Multi-Scale Video Longformer network (MSVL), built upon the 3D Longformer architecture featuring a “local attention + global features” attention mechanism, enabling us to reduce computational complexity while preserving global modeling capabilities. Specifically, MSVL gradually reduces the video feature resolution and increases the feature dimensions across four stages. In the lower layers of the network (stage 1, stage 2), we leverage local window attention to alleviate local redundancy and computational demands. Concurrently, global tokens are employed to retain global features. In the higher layers of the network (stage 3, stage 4), this local window attention evolves into a dense computation mechanism, enhancing overall performance. Finally, extensive experiments are conducted on UCF101 (97.6%), HMDB51 (72.9%), and the assembly action dataset (100.0%), demonstrating the effectiveness and efficiency of the MSVL.
Asynchronous and Slow-Wave Oscillatory States in Connectome-Based Models of Mouse, Monkey and Human Cerebral Cortex
Authors: Sacha Maria , Goldman Jennifer S. , Kusch Lionel , Destexhe Alain
Published: January 2024

Thanks to the availability of connectome data that map connectivity between multiple brain areas, it is now possible to build models of whole-brain activity. At the same time, advances in mean-field techniques have led to biologically based population models that integrate biophysical features such as membrane conductances or synaptic conductances. In this paper, we show that this approach can be used in brain-wide models of mice, macaques, and humans.We illustrate this approach by showing the transition from wakefulness to sleep, simulated using multi-scale models, in the three species. We compare the level of synchrony between the three species and find that the mouse brain displays a higher overall synchrony of slow waves compared to monkey and human brains. We show that these differences are due to the different delays in axonal signal propagation between regions associated with brain size differences between the species. We also make the program code—which provides a set of open-source tools for simulating large-scale activity in the cerebral cortex of mice, monkeys, and humans—publicly available.
Path Tracking Control with Constraint on Tire Slip Angles under Low-Friction Road Conditions
Authors: Lee Jaepoong , Yim Seongjin
Published: January 2024

This paper presents a method to design a path tracking controller with a constraint on tire slip angles under low-friction road conditions. On a low-friction road surface, a lateral tire force is easily saturated and decreases as a tire slip angle increases by a large steering angle. Under this situation, a path tracking controller cannot achieve its maximum performance. To cope with this problem, it is necessary to limit tire slip angles to a value where the maximum lateral tire force is achieved. The most commonly used controllers for path tracking, linear quadratic regulator (LQR) and model predictive control (MPC), are adopted as a controller design methodology. The control inputs of LQR and MPC are front and rear steering angles and control yaw moment, which have been widely used for path tracking. The constraint derived from tire slip angles is imposed on the steering angles of LQR and MPC. To fully verify the performance of the path tracking controller with the constraint on tire slip angles, a simulation is conducted on vehicle simulation software. From the simulation results, it is shown that the path tracking controller with the constraint on tire slip angles presented in this paper is quite effective for path tracking on low-friction road surface.
Application of the Polynomial Chaos Expansion to the Uncertainty Propagation in Fault Transients in Nuclear Fusion Reactors: DTT TF Fast Current Discharge
Authors: De Bastiani Marco , Aimetta Alex , Bonifetto Roberto , Dulla Sandra
Published: January 2024

Nuclear fusion reactors are composed of several complex components whose behavior may be not certain a priori. This uncertainty may have a significant impact on the evolution of fault transients in the machine, causing unexpected damage to its components. For this reason, a suitable method for the uncertainty propagation during those transients is required. The Monte Carlo method would be the reference option, but it is, in most of the cases, not applicable due to the large number of required, repeated simulations. In this context, the Polynomial Chaos Expansion has been considered as a valuable alternative. It allows us to create a surrogate model of the original one in terms of orthogonal polynomials. Then, the uncertainty quantification is performed repeatedly, relying on this much simpler and faster model. Using the fast current discharge in the Divertor Tokamak Test Toroidal Field (DTT TF) coils as a reference scenario, the following method has been applied: the uncertainty on the parameters of the Fast Discharge Unit (FDU) varistor disks is propagated to the simulated electrical and electromagnetic relevant effects. Eventually, two worst-case scenarios are analyzed from a thermal–hydraulic point of view with the 4C code, simulating a fast current discharge as a consequence of a coil quench. It has been demonstrated that the uncertainty on the inputs (varistor parameters) strongly propagates, leading to a wide range of possible scenarios in the case of accidental transients. This result underlines the necessity of taking into account and propagating all possible uncertainties in the design of a fusion reactor according to the Best Estimate Plus Uncertainty approach. The uncertainty propagation from input data to electrical, electromagnetic, and thermal hydraulic results, using surrogate models, is the first of its kind in the field of the modeling of superconducting magnets for nuclear fusion applications.
Critical Review of Techniques for Food Emulsion Characterization
Authors: Kupikowska-Stobba Barbara , Domagała Jacek , Kasprzak Mirosław M.
Published: January 2024

Emulsions have garnered significant attention within a variety of industries, including pharmaceuticals, food production, and cosmetics. The importance of emulsions across these sectors is attributed to their versatility and unique properties, such as increased interfacial area and the ability to deliver compounds insoluble in water or to mask the flavor of unpalatable ingredients. A comprehensive and precise assessment of the physicochemical properties, structural features, and stability of emulsions is an indispensable phase in the pursuit of new formulations and the improvement of manufacturing protocols. The characterization of emulsions encompasses an array of methodologies designed to determine their attributes, such as droplet size, distribution, concentration, surface charge, and others. In this review, we explore the techniques most frequently used to characterize emulsions and critically assess the significance each method holds in understanding the behavior and predicting the stability of emulsions. We elucidate the basic principles of these methods while emphasizing what information can be gathered from them, and how to effectively interpret this information to optimize the properties of emulsions, crucial from the standpoints of food and other industries, such as long-term stability and easy processing.
Recognizing Textual Inference in Mongolian Bar Exam Questions
Authors: Khaltarkhuu Garmaabazar , Batjargal Biligsaikhan , Maeda Akira
Published: January 2024

This paper examines how to apply deep learning techniques to Mongolian bar exam questions. Several approaches that utilize eight different fine-tuned transformer models were demonstrated for recognizing textual inference in Mongolian bar exam questions. Among eight different models, the fine-tuned bert-base-multilingual-cased obtained the best accuracy of 0.7619. The fine-tuned bert-base-multilingual-cased was capable of recognizing “contradiction”, with a recall of 0.7857 and an F1 score of 0.7674; it recognized “entailment” with a precision of 0.7750, a recall of 0.7381, and an F1 score of 0.7561. Moreover, the fine-tuned bert-large-mongolian-uncased showed balanced performance in recognizing textual inference in Mongolian bar exam questions, thus achieving a precision of 0.7561, a recall of 0.7381, and an F1 score of 0.7470 for recognizing “contradiction”.
An Acoustic Array Sensor Signal Recognition Algorithm for Low-Altitude Targets Using Multiple Five-Element Acoustic Positioning Systems with VMD
Authors: Song Chunhuan , Li Hanshan
Published: January 2024

To solve the problem in target acoustic signal processing and recognition when the target flies at a low altitude based on the acoustic positioning system, which is often affected by external interference and brings false information, this paper proposes a target signal processing and recognition algorithm for low-altitude target acoustic positioning based on variational modal decomposition and the test method of multiple five-element acoustic arrays. This algorithm uses VMD to decompose the target signal into modal components with different central frequencies and then performs wavelet threshold processing on the low-frequency part of the signal. After determining the remaining signal components and the low-frequency part’s threshold, the residual component is reconstructed. Based on the test principle and calculation model of the five-element acoustic positioning system, following processing of the low-altitude target acoustic positioning signal using variational modal decomposition, the cross-correlation function method is introduced to perform correlation operations on the basic array of five acoustic sensors and then obtain the time value and time difference of the target acoustic information in each acoustic sensor, ultimately determining the spatial position of the target. Finally, we used the data fusion processing method for target coordinates in multi-acoustic basic arrays to determine the actual target position. By comparing the results obtained using the high-speed camera method with those of the proposed approach, it was found that the average error in the test area of 100 × 100 m was less than 1 m.
Temperature Relaxation in Glass-Forming Materials under Local Fast Laser Excitations during Laser-Induced Microstructuring
Authors: Minakov Alexander , Schick Christoph
Published: January 2024

The ability to control the temperature distribution T(t,r) and the rate of temperature change Rt,r inside glasses is important for their microstructuring. The lattice temperature is considered at time t, exceeding the electron–phonon thermalization time, and at a distance r from the center of the model spherical heating zone. In order to describe thermal excitations, the heat capacity of glasses must be considered as a function of time due to its long-term relaxation. A method for the analytical calculation of T(t,r) and R(t,r) for glasses with dynamic heat capacity cdyn(t) is proposed. It is shown that during laser microstructuring, the local cooling rate −R(t,r) significantly depends on the time dispersion of cdyn(t). It has been established that at the periphery of the model heating zone of the laser beam focus, the local cooling rate can reach more than 1011 K/s. Strong cooling rate gradients were found at the periphery of the heating zone, affecting the microstructure of the material. This effect is significantly enhanced by the time dispersion of cdyn(t). The effect associated with this time dispersion is significant, even well above the glass transition temperature Tg, since even short relaxation times of the dynamic heat capacity cdyn(t) are significant.
A New Framework for Active Loss Reduction and Voltage Profile Enhancement in a Distributed Generation-Dominated Radial Distribution Network
Authors: Owosuhi Adedayo , Hamam Yskandar , Munda Josiah
Published: January 2024

In recent times, a significant amount of power loss and system instability due to high voltage deviation experienced by modern power systems, in addition to the pressing issues challenging the power industry such as pollution—especially the emission of greenhouse gases—and aging infrastructures, have posed a serious threat to system operations. Distributed generation has been identified as one main solution capable of reducing pollution when solar and wind power are used and, hence, rejuvenating dilapidated infrastructures and redeeming climatic changes. This paper presents a novel two-stage approach for the identification of suitable locations for DG placement and the sizing of DG for loss reduction and voltage stability enhancement. The first stage explored the use of a network structure to develop a coupling factor (CF) approach that was non-iterative in nature to determine suitable DG locations. In the second stage, the size of the DG was determined using the particle swarm optimization (PSO) algorithm. The main objective was to obtain an optimal voltage profile of the system under consideration while lowering the power loss in the system and ensuring network stability amidst DG incorporation. The model design, optimization and simulation were carried out using the MATLAB 2016a environment and the IEEE 33-bus test system, in which DG was integrated. The influence of increasing the level of DG placement in the system was then investigated. The forward/backward sweep method was applied to monitor the optimization process. The voltage profiles for both the base case when no DG was integrated and the case of incremental DG integration were considered. The results obtained for both single and multiple DG integration are compared with those obtained using the existing methods. The results show the efficiency and applicability of the new non-iterative scheme in the quick identification of DG locations for voltage profile enhancement and network real power loss reduction in radial distribution networks.
Paper-Mill Wastes for Bioethanol Production in Relation to Circular Economy Concepts: A Review
Authors: Or-Chen Dafna , Gerchman Yoram , Mamane Hadas , Peretz Roi
Published: January 2024

This review explores circular economy principles in regard to ethanol production from paper-mill sludge. Environmental sustainability and renewability over fossil fuels make second generation ethanol an attractive energy source in a rapidly growing population and consumption world. Paper sludge (PS), a by-product of the pulp and paper (P&P) industry, can no longer be recycled for paper production and is mainly disposed of in landfills. Therefore, it poses a major environmental challenge. However, it has shown potential as a valuable raw material for ethanol production, along with other pulp and paper products, due to its abundant availability and high cellulosic content. This waste-to-energy (WtE) technology for ethanol production is proposed as an alternative, aligning with circular economy concepts to maximize resource efficiency and minimize waste. This review underlines the circular economy aspects of bioethanol production within paper mill sludge management systems. Circular economy principles applied to ethanol production from PS offer a promising avenue for sustainable biofuel development that not only addresses waste management challenges but also enhances the overall environmental performance of biofuel production. Furthermore, economic benefits are described, highlighting the potential for job creation and community development.
Characteristics and Sensitivity Analysis of Ozone Pollution in a Typical Inland City in China
Authors: Hua Xiaohui , Wang Meng , Yao Zhen , Hao Run , Wang Hailin
Published: January 2024

In this research, long-term monitoring data from 2020 to 2023 were used to characterize O3 pollution in a typical inland city in northwest China (34°21′ N 109°30′ E), which indicated that ozone pollution yielded typical regular fluctuations and high ozone concentrations from April to September were observed. Ozone varied in the range of 16–176 μg/m3, and maximum peaks were found usually at 14:00–17:00 in June and July. Correlation analysis showed a significant positive relationship between ozone and temperature, with correlation coefficients of 0.93. The wind speed exhibits a similar variation as ozone. Meanwhile, negative correlations were not so notably observed among ozone, humidity, VOCs, and NOx. Finally, the empirical kinetic model OZIPR (Ozone Isopleth Plotting Program for Research) was employed to analyze the sensitivity relationship among ozone and precursor compounds by calculating EMKA (Empirical Kinetics Modeling Approach) curves. The EKMA analysis results showed that during the whole ozone pollution period, ozone formation is mainly dominated by VOCs due to all the ratios of VOCs/NOx which fell in the VOCs control region. Therefore, VOCs should be priority controlled and more measures should be taken for better ozone pollution control abatement.
Rainfall- and Irrigation-Induced Landslide Mechanisms in Loess Slopes: An Experimental Investigation in Lanzhou, China
Authors: Liu Wei , Bai Ruiqiang , Sun Xinran , Yang Fang , Zhai Weiji , Su Xing
Published: January 2024

To reveal the mechanism of rainfall- and irrigation-induced landslides in loess slopes within cold regions, a series of tests on loess samples subjected to different permeability durations were conducted, and the effects of rainfall on several performance indicators, including the permeability coefficient, composition, microstructure, soil–water characteristic curve, and the shear strength of the loess, were investigated. The results show that the permeability coefficient of the loess decreased by 68% after permeability testing. With increased permeability duration, there is a marked decrease in total dissolved solids, sand particles, and clay particles, contrasted with an increase in silt particles. This dynamic alters the original soil structure and impacts the soil–water characteristic curve of the loess. Additionally, rainwater infiltration heightens the effective saturation of the loess, in turn diminishing the shear strength of the loess as effective saturation increases. This reduction in shear strength is further intensified with extended infiltration time (or rainfall duration). A landslide is triggered once the shear strength diminishes to the level of the geostatic stress of the loess slope, and the influence of the rainfall-induced loss of soil shear strength should be taken into account during slope stability analysis. This study enhances the understanding of the initiation mechanisms of rainfall-induced landslides in loess slopes.
Benchmarking Water-Use Efficiency for Wheat at Leaf and Ecosystem Scales
Authors: Zhao Funian , Liu Jiang , Zhang Qiang , Zhang Liang , Qi Yue , Chen Fei
Published: January 2024

The processes coupled with carbon and water exchange are linked to crop assimilation, water consumption, controlling crop growth and development, and ultimately determining crop yield. Therefore, studying the characteristics of crop water constraints and their controlling factors at multiple scales is of great significance for regional and global food production stability and food security. Employing field observations and a comprehensive literature review, this study investigates the maximum water-use efficiency of wheat and its governing factors at both leaf and canopy (ecosystem) scales. The results demonstrate remarkable consistency and well-defined boundaries in maximum water-use efficiency across diverse climate regions and wheat varieties, both at the leaf and agricultural ecosystem scales. At the leaf scale, the maximum water-use efficiency of wheat was 4.5 μg C mg−1 H2O, while for wheat agricultural ecosystems, on a daily scale, the maximum water-use efficiency was 4.5 g C kg−1 H2O. Meanwhile, the maximum water-use efficiency of wheat agricultural ecosystems decreased continuously with increasing time scales, with values of 6.5, 4.5, 3.5, and 2 g C kg−1 H2O for instantaneous, daily, weekly, and monthly scales, respectively. Environmental factors, primarily vapor pressure deficit, light, and soil water content, exert significant control over leaf-level water-use efficiency. Similarly, the maximum water-use efficiency of agricultural ecosystems fluctuates in response to daily variations in meteorological elements. C3 crops like wheat exhibit remarkable resilience in their carbon–water exchange patterns across diverse environmental conditions. The findings in the current research can serve as a reference for improving crop water-use efficiency.
Effect of Partial Cation Replacement on Anode Performance of Sodium-Ion Batteries
Authors: He Shijiang , Wang Zidong , Qiu Wenbo , Zhao Huaping , Lei Yong
Published: January 2024

Due to their high specific capacity and long cycle life, bimetallic sulfides are the preferred choice of researchers as anodes in sodium-ion batteries (SIBs). However, studies indicate that this class of materials often requires expensive elements such as Co, Sb, Sn, etc., and their performance is insufficient with the use of inexpensive Fe, V alone. Therefore, there is a need to explore the relationship between metal cations and anode performance so that the requirements of cost reduction and performance enhancement can be met simultaneously. In this work, a series of partially replaced sulfides with different cation ratios have been prepared by a hydrothermal method followed by heat treatment. By partially replacing Co in NiCo sulfides, all samples show improved capacity and stability over the original NiCo sulfides. As a result, the metal elements have different oxidation states, which leads to a higher capacity through their synergistic effects on each other. Mn-NiCoS with 10% replacement showed satisfactory capacity (721.09 mAh g−1 at 300 mA g−1, 662.58 mAh g−1 after 20 cycles) and excellent cycle life (85.41% capacity retention after 1000 cycles at 2000 mA g−1).
Artificial Tear Instillation-Induced Changes in Corneal Topography
Authors: Lee Yunjin , Kim Tae Hun , Paik Hae Jung , Kim Dong Hyun
Published: January 2024

This study aims to compare changes of corneal topography (Galilei G4) before and after the instillation of artificial tears in patients with dry eye disease (DED). Corneal topography was performed in patients 1 min before and after artificial tear instillation. Two types of artificial tears were used: 1% polysorbate 80 (PSB) and 0.5% carboxymethylcellulose (CMC). Of 135 patients, PSB and CMC were instilled in 101 and 34 eyes, respectively. The average value of Sim K increased significantly after instillation (44.07 ± 2.26 diopter (D)) compared to before (43.90 ± 2.02 D, p = 0.006) the instillation of artificial tears. Mean Sim K astigmatism was statistically increased after PSB instillation (1.48 ± 2.17 D) compared to before instillation (1.31 ± 2.10 D, p = 0.049). An axis change of astigmatism 10° or more after artificial tear instillation was found in 51.9% of patients, and 30° or more in 20.0% of patients. Increased Sim K value and significant changes in the astigmatic axis in the corneal topography were observed after instillation of artificial tears in DED patients. PSB instillation had a greater effect on corneal keratometry values than CMC instillation.
Advancing Glaucoma Care: Integrating Artificial Intelligence in Diagnosis, Management, and Progression Detection
Authors: Zhu Yan , Salowe Rebecca , Chow Caven , Li Shuo , Bastani Osbert , O’Brien Joan M.
Published: January 2024

Glaucoma, the leading cause of irreversible blindness worldwide, comprises a group of progressive optic neuropathies requiring early detection and lifelong treatment to preserve vision. Artificial intelligence (AI) technologies are now demonstrating transformative potential across the spectrum of clinical glaucoma care. This review summarizes current capabilities, future outlooks, and practical translation considerations. For enhanced screening, algorithms analyzing retinal photographs and machine learning models synthesizing risk factors can identify high-risk patients needing diagnostic workup and close follow-up. To augment definitive diagnosis, deep learning techniques detect characteristic glaucomatous patterns by interpreting results from optical coherence tomography, visual field testing, fundus photography, and other ocular imaging. AI-powered platforms also enable continuous monitoring, with algorithms that analyze longitudinal data alerting physicians about rapid disease progression. By integrating predictive analytics with patient-specific parameters, AI can also guide precision medicine for individualized glaucoma treatment selections. Advances in robotic surgery and computer-based guidance demonstrate AI’s potential to improve surgical outcomes and surgical training. Beyond the clinic, AI chatbots and reminder systems could provide patient education and counseling to promote medication adherence. However, thoughtful approaches to clinical integration, usability, diversity, and ethical implications remain critical to successfully implementing these emerging technologies. This review highlights AI’s vast capabilities to transform glaucoma care while summarizing key achievements, future prospects, and practical considerations to progress from bench to bedside.
The Purinergic Nature of Pseudoxanthoma Elasticum
Authors: Kauffenstein Gilles , Martin Ludovic , Le Saux Olivier
Published: January 2024

Pseudoxanthoma Elasticum (PXE) is an inherited disease characterized by elastic fiber calcification in the eyes, the skin and the cardiovascular system. PXE results from mutations in ABCC6 that encodes an ABC transporter primarily expressed in the liver and kidneys. It took nearly 15 years after identifying the gene to better understand the etiology of PXE. ABCC6 function facilitates the efflux of ATP, which is sequentially hydrolyzed by the ectonucleotidases ENPP1 and CD73 into pyrophosphate (PPi) and adenosine, both inhibitors of calcification. PXE, together with General Arterial Calcification of Infancy (GACI caused by ENPP1 mutations) as well as Calcification of Joints and Arteries (CALJA caused by NT5E/CD73 mutations), forms a disease continuum with overlapping phenotypes and shares steps of the same molecular pathway. The explanation of these phenotypes place ABCC6 as an upstream regulator of a purinergic pathway (ABCC6 → ENPP1 → CD73 → TNAP) that notably inhibits mineralization by maintaining a physiological Pi/PPi ratio in connective tissues. Based on a review of the literature and our recent experimental data, we suggest that PXE (and GACI/CALJA) be considered as an authentic “purinergic disease”. In this article, we recapitulate the pathobiology of PXE and review molecular and physiological data showing that, beyond PPi deficiency and ectopic calcification, PXE is associated with wide and complex alterations of purinergic systems. Finally, we speculate on the future prospects regarding purinergic signaling and other aspects of this disease.
FLVCR1a Controls Cellular Cholesterol Levels through the Regulation of Heme Biosynthesis and Tricarboxylic Acid Cycle Flux in Endothelial Cells
Authors: Manco Marta , Ammirata Giorgia , Petrillo Sara , De Giorgio Francesco , Fontana Simona , Riganti Chiara , Provero Paolo , Fagoonee Sharmila , Altruda Fiorella , Tolosano Emanuela
Published: January 2024

Feline leukemia virus C receptor 1a (FLVCR1a), initially identified as a retroviral receptor and localized on the plasma membrane, has emerged as a crucial regulator of heme homeostasis. Functioning as a positive regulator of δ-aminolevulinic acid synthase 1 (ALAS1), the rate-limiting enzyme in the heme biosynthetic pathway, FLVCR1a influences TCA cycle cataplerosis, thus impacting TCA flux and interconnected metabolic pathways. This study reveals an unexplored link between FLVCR1a, heme synthesis, and cholesterol production in endothelial cells. Using cellular models with manipulated FLVCR1a expression and inducible endothelial-specific Flvcr1a-null mice, we demonstrate that FLVCR1a-mediated control of heme synthesis regulates citrate availability for cholesterol synthesis, thereby influencing cellular cholesterol levels. Moreover, alterations in FLVCR1a expression affect membrane cholesterol content and fluidity, supporting a role for FLVCR1a in the intricate regulation of processes crucial for vascular development and endothelial function. Our results underscore FLVCR1a as a positive regulator of heme synthesis, emphasizing its integration with metabolic pathways involved in cellular energy metabolism. Furthermore, this study suggests that the dysregulation of heme metabolism may have implications for modulating lipid metabolism. We discuss these findings in the context of FLVCR1a’s potential heme-independent function as a choline importer, introducing additional complexity to the interplay between heme and lipid metabolism.
Mapping Expression Quantitative Trait Loci Targeting Candidate Genes for Pregnancy in Beef Cows
Authors: Diniz Wellison J. S. , Afonso Juliana , Kertz Nicholas C. , Dyce Paul W. , Banerjee Priyanka
Published: January 2024

Despite collective efforts to understand the complex regulation of reproductive traits, no causative genes and/or mutations have been reported yet. By integrating genomics and transcriptomics data, potential regulatory mechanisms may be unveiled, providing opportunities to dissect the genetic factors governing fertility. Herein, we identified regulatory variants from RNA-Seq data associated with gene expression regulation in the uterine luminal epithelial cells of beef cows. We identified 4676 cis and 7682 trans eQTLs (expression quantitative trait loci) affecting the expression of 1120 and 2503 genes, respectively (FDR < 0.05). These variants affected the expression of transcription factor coding genes (71 cis and 193 trans eQTLs) and genes previously reported as differentially expressed between pregnant and nonpregnant cows. Functional over-representation analysis highlighted pathways related to metabolism, immune response, and hormone signaling (estrogen and GnRH) affected by eQTL-regulated genes (p-value ≤ 0.01). Furthermore, eQTLs were enriched in QTL regions for 13 reproduction-related traits from the CattleQTLdb (FDR ≤ 0.05). Our study provides novel insights into the genetic basis of reproductive processes in cattle. The underlying causal mechanisms modulating the expression of uterine genes warrant further investigation.
Role of Incretins in Muscle Functionality, Metabolism, and Body Composition in Breast Cancer: A Metabolic Approach to Understanding This Pathology
Authors: Martínez-Herrera Brenda-Eugenia , Muñoz-García Michelle-Guadalupe , José-Ochoa Laura-Liliana , Quiroga-Morales Luis-Aarón , Cervántes-González Luz-María , Mireles-Ramírez Mario-Alberto , Delgadillo-Cristerna Raúl , Nuño-Guzmán Carlos-M. , Leal-Cortés Caridad-Aurea , Portilla-de-Buen Eliseo , Hernández Benjamín Trujillo , Gómez-Sánchez Eduardo , Velázquez-Flores Martha-Cecilia , Salazar-Páramo Mario , Ochoa-Plascencia Miguel-Ricardo , Sat-Muñoz Daniel , Balderas-Peña Luz-Ma.-Adriana
Published: January 2024

A poorly studied issue in women with breast cancer is the role of incretins (GIP (glucose-dependent insulinotropic polypeptide) and GLP-1 (glucagon-like peptide-1)) in the quantity and quality of muscle mass in lean and obese individuals. The current report aims to analyze the patterns of association and the role of incretin in muscle functionality and body composition in women with cancer compared with healthy women (mammography BI-RADS I or II) to elucidate whether GIP and GLP-1 can be used to estimate the risk, in conjunction with overweight or obesity, for breast cancer. We designed a case–control study in women with a breast cancer diagnosis confirmed by biopsy in different clinical stages (CS; n = 87) and healthy women with a mastography BI-RADS I or II within the last year (n = 69). The women were grouped according to body mass index (BMI): lean (<25 kg/m2BS), overweight (≥25–<30 kg/m2BS), and obese (≥30 kg/m2BS). We found that GLP-1 and GIP levels over 18 pg/mL were associated with a risk of breast cancer (GIP OR = 36.5 and GLP-1 OR = 4.16, for the entire sample), particularly in obese women (GIP OR = 8.8 and GLP-1 OR = 6.5), and coincidentally with low muscle quality indexes, showed an association between obesity, cancer, incretin defects, and loss of muscle functionality.
Impaired Mitochondrial Network Morphology and Reactive Oxygen Species Production in Fibroblasts from Parkinson’s Disease Patients
Authors: Kritskaya Kristina A. , Fedotova Evgeniya I. , Berezhnov Alexey V.
Published: January 2024

The mitochondrial network (MN) is a dynamic structure undergoing constant remodeling in the cell. It is assumed that perturbations to the MN may be associated with various pathologies, including Parkinson’s disease (PD). Using automatic image analysis and super-resolution microscopy, we have assessed the MN parameters in fibroblasts from patients with established hereditary PD mutations (associated with PINK1, LRRK2, and α-synuclein, as well as PINK1 and Parkin proteins simultaneously) under normal conditions and after hydrogen peroxide-induced stress. Fibroblasts with the Pink1/Parkin mutation are most different in morphology to fibroblasts obtained from conditionally healthy donors: the MN is larger, and it contains longer mitochondria and accumulated individual mitochondria. In addition to MN, we evaluated other cellular parameters, such as cytosolic and mitochondrial ROS production and mitochondrial membrane potential. It has been shown that mitochondria of fibroblasts with mutations in genes encoding PINK1, α-synuclein, and Pink/Parkin tend towards hyperpolarization and cytosolic ROS overproduction, while mitochondrial ROS production was higher only in fibroblasts with PINK1 and α-synuclein mutations.
Chitosan Nanoparticles Loaded with Quercetin and Valproic Acid: A Novel Approach for Enhancing Antioxidant Activity against Oxidative Stress in the SH-SY5Y Human Neuroblastoma Cell Line
Authors: Canbolat Fadime , Demir Neslihan , Yayıntas Ozlem Tonguc , Pehlivan Melek , Eldem Aslı , Ayna Tulay Kilicaslan , Senel Mehmet
Published: January 2024

Background: Multiple drug-delivery systems obtained by loading nanoparticles (NPs) with different drugs that have different physicochemical properties present a promising strategy to achieve synergistic effects between drugs or overcome undesired effects. This study aims to develop a new NP by loading quercetin (Que) and valproic acid (VPA) into chitosan. In this context, our study investigated the antioxidant activities of chitosan NPs loaded with single and dual drugs containing Que against oxidative stress. Method: The synthesis of chitosan NPs loaded with a single (Que or VPA) and dual drug (Que and VPA), the characterization of the NPs, the conducting of in vitro antioxidant activity studies, and the analysis of the cytotoxicity and antioxidant activity of the NPs in human neuroblastoma SH-SY5Y cell lines were performed. Result: The NP applications that protected cell viability to the greatest extent against H2O2-induced cell damage were, in order, 96 µg/mL of Que-loaded chitosan NP (77.30%, 48 h), 2 µg/mL of VPA-loaded chitosan NP (70.06%, 24 h), 96 µg/mL of blank chitosan NP (68.31%, 48 h), and 2 µg/mL of Que- and VPA-loaded chitosan NP (66.03%, 24 h). Conclusion: Our study establishes a successful paradigm for developing drug-loaded NPs with a uniform and homogeneous distribution of drugs into NPs. Chitosan NPs loaded with both single and dual drugs possessing antioxidant activity were successfully developed. The capability of chitosan NPs developed at the nanometer scale to sustain cell viability in SH-SY5Y cell lines implies the potential of intranasal administration of chitosan NPs for future studies, offering protective effects in central nervous system diseases.
Short-Term Effect of Auditory Stimulation on Neural Activities: A Scoping Review of Longitudinal Electroencephalography and Magnetoencephalography Studies
Authors: Kobayashi Kanon , Shiba Yasushi , Honda Shiori , Nakajima Shinichiro , Fujii Shinya , Mimura Masaru , Noda Yoshihiro
Published: January 2024

Explored through EEG/MEG, auditory stimuli function as a suitable research probe to reveal various neural activities, including event-related potentials, brain oscillations and functional connectivity. Accumulating evidence in this field stems from studies investigating neuroplasticity induced by long-term auditory training, specifically cross-sectional studies comparing musicians and non-musicians as well as longitudinal studies with musicians. In contrast, studies that address the neural effects of short-term interventions whose duration lasts from minutes to hours are only beginning to be featured. Over the past decade, an increasing body of evidence has shown that short-term auditory interventions evoke rapid changes in neural activities, and oscillatory fluctuations can be observed even in the prestimulus period. In this scoping review, we divided the extracted neurophysiological studies into three groups to discuss neural activities with short-term auditory interventions: the pre-stimulus period, during stimulation, and a comparison of before and after stimulation. We show that oscillatory activities vary depending on the context of the stimuli and are greatly affected by the interplay of bottom-up and top-down modulational mechanisms, including attention. We conclude that the observed rapid changes in neural activitiesin the auditory cortex and the higher-order cognitive part of the brain are causally attributed to short-term auditory interventions.
The Evolution and Future Directions of Green Buildings Research: A Scientometric Analysis
Authors: Wang Chongqing , Che Yanhong , Xia Mingqian , Lin Chenghan , Chen Yuqi , Li Xi , Chen Hong , Luo Jingpeng , Fan Gongduan
Published: January 2024

Economic development and urbanization naturally give rise to expanding demand for new buildings, whose construction and operation inevitably lead to significant increases in energy consumption and greenhouse gas emissions. To better conserve resources and protect the environment, technologies for green buildings have evolved significantly in the past two decades. In this study, a scientometric analysis of green buildings research from 2003 to 2023 was performed using CiteSpace. A total of 1986 articles retrieved from the Web of Science (WoS) core collection database were used as the data source for an in-depth analysis of research trends, hotspots, and future directions, showing changes in publication numbers, core journals, key countries, and institutions that have made remarkable contributions in this field. The results showed that the field of green buildings research is in a phase of rapid growth. The current research hotspots include the adoption of the green buildings paradigm, rating systems, energy performance, greenhouse gas emissions, indoor environmental quality, and green roofs/walls. Based on the keywords citation bursts and literature review, we believe that government promotion measures, use of renewable energy, integration with plants, and application of artificial intelligence (AI) in green buildings will be the most promising development directions in the future.
Exploring the Mechanisms Influencing Users’ Willingness to Pay for Green Real Estate Projects in Asia Based on Technology Acceptance Modeling Theory
Authors: Wang Siqin , Chen Sen , Nah Ken
Published: January 2024

In contrast to the extensive research on the impact of green buildings on character, property values, energy efficiency, and environmental quality, less emphasis has been placed on understanding the factors driving users’ selection of green real estate projects, an essential element for the future business sustainability of such buildings. This study investigates the connection between various user motivations, such as perceived usefulness, perceived usability, a sense of responsibility, awareness of consequences, and perceived behavioral control, and their impact on Asian users’ willingness to pay for green real estate projects. These users include those who have already invested in and are considering long-term investment in such projects. An empirical methodology, integrating these motivations with a technology acceptance model, is proposed to assess their willingness to pay either through renting or buying. The study applies structural equation modeling to analyze sample data against a hypothetical model, thereby testing the hypotheses. It concludes that, in influencing green real estate projects, the following apply: (1) perceived usefulness and perceived behavioral control significantly shape Asian users’ attitudes towards financial commitment; (2) perceived usability directly influences their perceived usefulness; (3) awareness of consequences acts as a mediating factor in the relationship between perceived usability and behavioral control among these users. Moreover, the study offers strategic design recommendations for future green real estate projects, informed by the diverse motivational factors of Asian users, to guide their purchasing and selection decisions.
Axial Compression Behavior of Elliptical Concrete-Filled Steel Tube Composite Short Columns with Encased Steel Considering Spherical-Cap Gap
Authors: Jiang Liangqin , Ma Liang , Hou Xiaomeng , Ji Jing , Zhang Huiling , Zhang Zhanbin , Liang Yuan , Chu Xuan
Published: January 2024

This study explored the axial compression behavior of elliptical concrete-filled steel tubes with encased steel considering spherical-cap gap (GSECFST) composite short columns. We designed 25 composite column specimens by varying the steel tube yield strength (fty), steel skeleton yield strength (fsy), concrete cubic compression strength (fcu), steel tube thickness (t), steel skeleton sectional area (As), the long and short half-axis ratio (a/b), the gap ratio (Xsg), and the slenderness ratio (λ). Based on the nonlinear constitutive models of the materials and the nonlinear contact effect among materials, the ABAQUS 6.20 finite element software established the refined finite element models of these composite short columns. Also, the rationality of the finite element modeling with a spherical-cap gap was verified by comparing it with the existing experimental results. The influence regularity of various parameters on the load (N)-displacement (Δ) curves, bearing capacity, initial stiffness, and ductility of the composite short columns was obtained. In addition, the failure modes, N-Δ process, sectional strain distribution, and gap feature index of the constraint partition model for GSECFST axial compression short columns were revealed. The results showed a weakened interaction between the elliptical steel tube and concrete. Also, the axial compression bearing capacity, initial stiffness, and core concrete ductility were reduced because of the spherical-cap gap. As fty, fsy, fc, and Asy increased, the axial bearing capacity, initial stiffness, and ductility of GSECFST composite short columns improved significantly but decreased with increasing of a/b, Xsg, and λ. When the gap ratio of the spherical crown was less than 4%, the outer steel tubes in the mid-span area of the GSECFST composite short columns buckled in the direction of the elliptical short axis under axial compression, and the concrete expanded outward and crushed. The failures were similar to those of the specimens without the spherical-cap gap. Based on the sectional constraint partition model, we propose the calculation formula of axial compression bearing capacity for GSECFST composite short columns. Consequently, this study is a reference for the elastic-plastic analysis of frame systems with similar composite columns.
Semiautomated Primary Layout Definition with a Point Cloud for Building-Envelope Renovation
Authors: Iturralde Kepa , Gambao Ernesto , Bock Thomas
Published: January 2024

Prefabricated modules are being used to renovate the building envelope. However, compared to manual methods, the design and prefabricated module’s definition is time consuming. Therefore, it is necessary to improve the efficiency of the prefabricated layout definition processes by incorporating automation and computational design. The purpose of this paper is to present a semi-automated definition of the layout of the prefabricated modules with the only input of the existing building facade being the Point Cloud. In this research, a novel step-by-step workflow was developed. More precisely, an algorithm was developed that processes the coordinates of each point of the cloud and generates the layout of the prefabricated modules. To validate the workflow and the algorithm, four facades were tested, considering two parameters: (a) working time and (b) output accuracy. According to the results, it was concluded that spending more time achieving an accurate laser data acquisition can be a good strategy to obtain the primary layout with sufficient precision.