bluuebunny/crossref_metadata_2025 huggingface dataset:
Safety and Feasibility of Combining On-Demand Selective Locoregional Treatment with First-Line Atezolizumab Plus Bevacizumab for Patients with Unresectable Hepatocellular Carcinoma
Authors: Nakabori Tasuku , Higashi Sena , Abe Yutaro , Mukai Kaori , Ikawa Toshiki , Konishi Koji , Maeda Noboru , Nakanishi Katsuyuki , Hasegawa Shinichiro , Wada Hiroshi , Ohkawa Kazuyoshi
Published: March 2024

Various locoregional treatments for localized hepatocellular carcinoma (HCC) have been developed. This retrospective study investigated the safety and feasibility of combining on-demand selective locoregional treatment for residual lesions after tumor shrinkage (complete response [CR] oriented) or for solitary or few drug-resistant lesions (progressive disease (PD) salvage) with first-line atezolizumab plus bevacizumab (atezo/bev) for unresectable HCC. Twenty-nine patients with unresectable HCC were included. Fourteen locoregional treatments were performed (CR oriented, 7; PD salvage, 7) in ten patients in the combination-therapy group. All patients in the combination-therapy group successfully achieved a CR or PD salvage status after the planned locoregional treatment. The objective response rate of the combination-therapy group (80.0%) was higher than that of the atezo/bev alone group (21.1%; p = 0.005). Progression-free survival (PFS) and overall survival (OS) were longer in the combination group (medians for PFS and OS not reached) than in the atezo/bev alone group (median PFS, 7.4 months; median OS, 19.8 months) (PFS, p = 0.004; OS, p < 0.001). The albumin–bilirubin score did not change, and no severe complications occurred after locoregional treatment. When performed in a minimally invasive manner, on-demand selective locoregional treatment combined with first-line atezo/bev could be safe and feasible for unresectable HCC.
Quality Index for Assessment of the Mechanical Condition of Transformers’ Active Part with Frequency Response and Vibroacoustic Measurements
Authors: Kornatowski Eugeniusz , Banaszak Szymon , Molenda Paweł
Published: March 2024

The paper describes the application of the numerical tool quality index for an objective evaluation of complementary frequency response analysis (FRA) and vibroacoustic method (VM) test methods. These diagnostic methods are used in the industrial practice of transformer diagnostics for the assessment of the mechanical condition of windings and a core. The quality index is based on a numerical comparison of the curve obtained from measurements and the reference curve in a frequency domain. The quality index is based on estimators for the covariance, variance, and expected values. First, both methods of analysis were applied to a group of transformers of similar construction, leading conclusions on quality index values being quickly drawn. Next, it was applied to another transformer’s FRA and VM measurement results. The results showed problems with its mechanical condition, thus confirming that the proposed methods can be used in the practical assessment of transformers with these two diagnostic methods. The assessment of transformer’s active-part mechanical condition with complementary FRA and VM methods can be performed much more easily with the proposed quality indices.
The Experimental Investigation of the Effects on the Combustion, Performance, and Emission Characteristics of an RCCI Engine Using Methanol/Diesel Fuel
Authors: Temur Mustafa , Sayin Cenk , Yilmaz Ilker Turgut
Published: March 2024

Reactivity-controlled compression ignition (RCCI) combustion is considered one of the most promising low-temperature combustion (LTC) concepts aimed at reducing greenhouse gases for the transportation and power generation sectors. RCCI combustion mode is achieved by combining different fuel types with low and high temperatures. The aim of this study is to investigate combustion characteristics and reduce nitrogen oxide (NOx) and carbon dioxide (CO2) emissions. In this experimental study, the effects of the RCCI strategy using methanol/diesel fuel on combustion characteristics (ignition delay, combustion duration), engine performance (brake-specific fuel consumption and brake-specific energy consumption), and emissions were examined in a four-cylinder, turbocharged, dual-fuel engine. The experiments were conducted at a constant speed of 1750 rpm at partial loads (40 Nm, 60 Nm, 80 Nm, and 100 Nm). The test results obtained with diesel fuel were compared with the test results obtained with methanol at different mass flow rates. When the results were examined, the minimum ignition delay (ID) occurred at 40 Nm torque, 5.63 crank angle (CA) with M12 fuel, while the maximum ID occurred with M26 fuel at 80 Nm torque, showing an increasing trend as engine load (EL) increased. The highest combustion time (CD) was achieved with M26 fuel at 100 Nm torque, whereas the lowest was achieved with the same fuel (M26) at 40 Nm. While the minimum brake-specific fuel consumption (bsfc) was 45.9 g/kWh for conventional diesel fuel at 40 Nm, the highest bsfc was 104.88 g/kWh for 100 Nm with M26 fuel. Generally, bsfc tends to increase with increasing load. Brake-specific energy consumption (bsec) had the lowest value of 1950.58 kJ/kWh with conventional diesel fuel at 40 Nm and the highest value of 4034.69 kJ/kWh with M26 fuel at 100 Nm. As the methanol content increased, significant improvements were observed in (NOx) and (CO2) emissions, while hydrocarbon (HC) and oxygen (O2) emissions increased as well. Smoke emissions decreased at low loads but tended to increase at high loads.
Addressing Confidence in Modeling of Contrail Formation from E-Fuels in Aviation Using Large Eddy Simulation Parametrization
Authors: Cabrera Eduardo , de Sousa João M. Melo
Published: March 2024

As it becomes increasingly necessary to reduce aviation-related emissions, condensation trails present an additional challenge. These are arguably responsible for the largest contribution to radiative forcing in the sector, but the phenomenon is still not as well understood as those involving other agents. The present study employs a large eddy simulation (LES) parametrization to validate a previously developed contrail model in order to assess the feasibility of a multi-model approach to increase confidence in simulations of contrail cirrus formation. Subsequently, the computational model was used to analyze the impact of e-fuels in contrail dynamics, resulting in reductions of over 7% and 14%, respectively, in average contrail lifetime and optical depth, with such improvements increasing if higher blending limits are utilized. This confirmed the potential for e-fuels as the most viable option for near-future large-scale implementations among all sustainable aviation fuel alternatives.
Electromagnetic Vibration and Noise Analysis of Linear Phase-Shifting Transformer
Authors: Yan Dongao , Zhao Jinghong , Yan Sinian , Wang Hanming , Zhou Changduo
Published: March 2024

The advantages of adjustable angle phase-shifting and great expansibility make the linear phase-shifting transformer a novel type of power conversion device with a wide range of potential applications. However, during the procedure, there is a lot of noise. For the purposes of transformer design and vibration and noise reduction, it is crucial to investigate its electromagnetic vibration and noise. In this paper, the radial electromagnetic force wave considering the influence of the end effect as the source of the noise of the linear phase-shifting transformer was deduced and calculated. Based on this, the spectrum and space–time properties of the radial electromagnetic force waves were simulated and verified. Additionally, a finite element model was created using the Ansys Workbench 2022R1 platform to study the electromagnetic vibration and noise of the linear phase-shifting transformer. A joint simulation of the electromagnetic, structural, and sound fields was then performed. First, the transformer’s natural frequency was determined by modal analysis. After that, the transformer’s structure and the results of the transient electromagnetic field computation were combined and a harmonic response analysis was conducted to determine the vibration acceleration spectrum. Finally, in order to solve the sound pressure field, the transformer’s boundary vibration acceleration was coupled to the air domain. Furthermore, an analysis was conducted to determine the noise distribution surrounding the linear phase-shifting transformer. The joint simulation findings demonstrate that the linear phase-shifting transformer’s resonance, which produces larger electromagnetic vibration and noise, is indeed caused by the radial electromagnetic force. Simultaneously, the impact of the LPST core’s fixed components on the electromagnetic vibration and noise of the core was examined.
Spinning of Carbon Nanofiber/Ni–Cu–S Composite Nanofibers for Supercapacitor Negative Electrodes
Authors: Li Qiong , Wang Yu , Wei Ganghui , Fang Xiaorong , Lan Ni , Zhao Yonggang , Liu Qiming , Lin Shumei , He Deyan
Published: March 2024

The preparation of composite carbon nanomaterials is one of the methods for improving the electrochemical performance of carbon-based electrode materials for supercapacitors. However, traditional preparation methods are complicated and time-consuming, and the binder also leads to an increase in impedance and a decrease in specific capacitance. Therefore, in this work, we reduced Ni-Cu nanoparticles on the surface of nitrogen-doped carbon nanofibers (CNFs) by employing an electrostatic spinning method combined with pre-oxidation and annealing treatments. At the same time, Ni-Cu nanoparticles were vulcanized to Ni–Cu–S nanoparticles without destroying the structure of the CNFs. The area-specific capacitance of the CNFs/Ni–Cu–S–300 electrode reaches 1208 mF cm−2 at a current density of 1 mA cm−2, and the electrode has a good cycling stability with a capacitance retention rate of 76.5% after 5000 cycles. As a self-supporting electrode, this electrode can avoid the problem of the poor adhesion of electrode materials and the low utilization of active materials due to the inactivity of the binder and conductive agent in conventional collector electrodes, so it has excellent potential for application.
Optimizing Building Short-Term Load Forecasting: A Comparative Analysis of Machine Learning Models
Authors: Koukaras Paraskevas , Mustapha Akeem , Mystakidis Aristeidis , Tjortjis Christos
Published: March 2024

The building sector, known for its high energy consumption, needs to reduce its energy use due to rising greenhouse gas emissions. To attain this goal, a projection for domestic energy usage is needed. This work optimizes short-term load forecasting (STLF) in the building sector while considering several variables (energy consumption/generation, weather information, etc.) that impact energy use. It performs a comparative analysis of various machine learning (ML) models based on different data resolutions and time steps ahead (15 min, 30 min, and 1 h with 4-step-, 2-step-, and 1-step-ahead, respectively) to identify the most accurate prediction method. Performance assessment showed that models like histogram gradient-boosting regression (HGBR), light gradient-boosting machine regression (LGBMR), extra trees regression (ETR), ridge regression (RR), Bayesian ridge regression (BRR), and categorical boosting regression (CBR) outperformed others, each for a specific resolution. Model performance was reported using R2, root mean square error (RMSE), coefficient of variation of RMSE (CVRMSE), normalized RMSE (NRMSE), mean absolute error (MAE), and execution time. The best overall model performance indicated that the resampled 1 h 1-step-ahead prediction was more accurate than the 15 min 4-step-ahead and the 30 min 2-step-ahead predictions. Findings reveal that data preparation is vital for the accuracy of prediction models and should be model-adjusted.
Analyzing Supply Reliability Incentive in Pricing Regulation of Electricity Distribution Operators
Authors: Seppälä Joel , Järventausta Pertti
Published: March 2024

In support of the global green transition, numerous policies have been introduced to efficiently address the increasing demand for reliable electricity. However, the impacts of these policies have received limited attention, despite the potential for unsuccessful policy targets to introduce inefficiencies into the energy system, subsequently diminishing societal wealth. This study bridges this research gap by conducting a comprehensive examination of a supply reliability incentive within electricity pricing regulation, aiming to contribute new insights for policy assessments. Analyzing data from all electricity distribution operators within a single jurisdiction, the study investigates the volume and distribution of economic steering to elucidate the overall societal impact. The findings suggest a rewarding system for positive developments in indices, regardless of the absolute interruption index levels, highlighting the importance of precise variable definitions in implementing incentive mechanisms. The assessment tools developed for this study will be valuable for further regulation and policy assessments.
Model for Identification of Electrical Appliance and Determination of Patterns Using High-Resolution Wireless Sensor NETWORK for the Efficient Home Energy Consumption Based on Deep Learning
Authors: Ulloa-Vásquez Fernando , Heredia-Figueroa Victor , Espinoza-Iriarte Cristóbal , Tobar-Ríos José , Aguayo-Reyes Fernanda , Carrizo Dante , García-Santander Luis
Published: March 2024

The growing demand for electricity and the constant increase in electricity rates have intensified the interest of residential and non-residential energy consumers to reduce their energy consumption. The introduction of non-conventional renewable energies (photovoltaic and wind, in the residential case) demands new proposals to obtain a home energy management system (HEMS), which allows reducing the use of electrical energy. This article incorporates artificial intelligence techniques to demand response, allowing control, switching, turning on and off of appliances, modifying and reducing consumption, and achieving improvements in the quality of life in the home. In addition, an architecture based on a smart socket and an artificial intelligence model that recognizes the consumption of electrical appliances in high resolution (sampling every 10 s) is proposed. The system uses the Wi-Fi communication protocol, ensuring that the smart sockets wirelessly provide the data obtained to the public cloud. The use of Deep Learning allows us to obtain a central control model of the home, which, when interconnected to the smart electrical distribution networks of companies, could generate a positive impact on the environmental effects and CO2 reduction.
An Ethane-Based CSI Process and Two Types of Flooding Process as a Hybrid Method for Enhancing Heavy Oil Recovery
Authors: Li Yishu , Du Zhongwei , Wang Bo , Ding Jiasheng , Zeng Fanhua
Published: March 2024

Combining multiple secondary oil recovery (SOR)/enhanced oil recovery (EOR) methods can be an effective way to maximize oil recovery from heavy oil reservoirs; however, previous studies typically focus on single methods. In order to optimize the combined process of ethane-based cyclic solvent injection (CSI) and water/nanoparticle-solution flooding, a comprehensive understanding of the impact of injection pressure, water, and nanoparticles on CSI performance is crucial. This study aims to provide such understanding through experimental evaluation, advancing the knowledge of EOR methods for heavy oil recovery. Three approaches (an ethane-based CSI process, water flooding, and nanoparticle-solution flooding) were applied through a cylindrical sandpack model with a length of 95.0 cm and a diameter of 3.8 cm. Test 1 conducted an ethane-based CSI process only. Test 2 conducted a combination approach of CSI–water flooding–CSI–nanoparticle-solution flooding–CSI. Specifically, the injection pressure of the first CSI phase in Test 2 was gradually increased from 3500 to 5500 kPa. The second and the third CSI phases had the same injection pressure as Test 1 at 5500 kPa. The CSI process ceased once the oil recovery was less than 0.5% of the original oil in place (OOIP) in a single cycle. Results show that the ethane-based CSI process is sensitive to injection pressure. A high injection pressure is crucial for optimal oil recovery. The first CSI phase in Test 2, where the injection pressure was increased gradually, resulted in a 2.9% lower oil recovery and five times as much ethane consumption compared to Test 1, which applied a high injection pressure. It was also found that water flooding improved the oil recovery in the CSI process. In Test 2, the oil recovery factor of the second CSI phase increased by 57% after the water flooding process, which is likely due to the formation of water channels and a dispersed oil phase that increased the contact area between ethane and oil. Although the nanoparticle-solution flooding only had 0.3% oil recovery, after that the third CSI phase stimulated another 10.8% of OOIP even when the water saturation achieved more than 65%. This demonstrated that the addition of nanoparticles can maintain the stability of the foam and enhance the transfer of ethane to the heavy oil. Finally, Test 2 reached a total oil recovery factor of 76.1% on a lab scale, an increase of 45% compared to the single EOR method, which proved the combination process is an efficient method to develop a heavy oil field.
Small Disturbance Stability Analysis of Onshore Wind Power All-DC Power Generation System Based on Impedance Method
Authors: Wang Tao , Li Fengting , Yin Chunya , Jin Guixin
Published: March 2024

The Onshore Wind Power All-DC Generation System (OWDCG) is designed to integrate with renewable energy sources by modifying the grid structure. This adaptation supports the grid infrastructure and addresses the challenges of large-scale wind power AC collection and harmonic resonance during transmission. Crucially, small disturbance stability parameters are essential for ensuring the system’s stable operation. Unlike conventional power systems, the OWDCG exhibits strong coupling between subsystems, accentuating the small disturbance stability issue due to the dynamic nature of its converter control system. The impedance method facilitates the decomposition of such systems into subsystems, offering insights into the destabilization mechanism through the lens of negative impedance contribution. This approach is conducive to conducting small disturbance stabilization analyses. To tackle this issue, the initial step involves deriving the input and output equivalent impedance models of the subsystem, considering the topological structure, control features, and operational dynamics of the OWDCG. Subsequently, the impact of circuit and control parameters on the system’s impedance characteristics and small-disturbance stability is examined through Bode diagrams and Nyquist curves. This analysis identifies critical parameters for small-disturbance stability, guiding the stable operation and parameter optimization of the OWDCG. The analysis highlights that the main control strategies for stability are the Modular Multilevel Converter (MMC) DC voltage control and the inner-loop current control gain. Validation of the theoretical findings is achieved through simulation results using PSCAD/EMTDC.
European Green Deal: Justification of the Relationships between the Functional Indicators of Bioenergy Production Systems Using Organic Residential Waste Based on the Analysis of the State of Theory and Practice
Authors: Tryhuba Inna , Tryhuba Anatoliy , Hutsol Taras , Lopushniak Vasyl , Cieszewska Agata , Andrushkiv Oleh , Barabasz Wiesław , Pikulicka Anna , Kowalczyk Zbigniew , Vasyuk Vyacheslav
Published: March 2024

Based on the analysis conducted on the state of theory and practice, the expediency of assessing the relationships between the functional indicators of bioenergy production systems using the organic waste of residential areas is substantiated in the projects of the European Green Deal. It is based on the use of existing results published in scientific works, as well as on the use of methods of system analysis and mathematical modeling. The proposed approach avoids limitations associated with the one-sidedness of sources or subjectivity of data and also ensures complete consideration of various factors affecting the functional indicators of the bioenergy production system from the organic waste of residential areas. Four types of organic waste generated within the territory of residential areas are considered. In our work, we used passive experimental methods to collect data on the functional characteristics of bioenergy production systems, mathematical statistics methods to process and interpret trends in the functional characteristics of bioenergy production systems using municipal organic waste, and mathematical modeling methods to develop mathematical models that reflect the patterns of change in the functional characteristics of bioenergy production systems. The results indicate the presence of dependencies with close correlations. The resulting dependencies can be used to optimize processes and increase the efficiency of bioenergy production. It was found that: (1) yard waste has the highest volume of the total volume of solid organic substances but has a low yield of biogas and low share of methane production; (2) food waste has the highest yield of biogas and, accordingly, the highest share of methane production; (3) mixed organic waste has the lowest volume of the total volume of solid organic substances and the lowest content of volatile organic substances. The amount of electricity and thermal energy production varies by type of organic waste, with mixed organic waste having a higher average amount of electricity production compared to other types of waste. It was established that the production volume of the solid fraction (biofertilizer) is also different for different types of organic waste. Less solid fraction is produced from food waste than from yard waste. The obtained research results are of practical importance for the development of sustainable bioenergy production from organic waste in residential areas during the implementation of the European Green Deal projects. They provide further research on the development of effective models for determining the rational configuration of bioenergy production systems using organic waste for given characteristics of residential areas.
Dominant Tree Species and Their Age Groups Drive Forest Carbon Storage in Wuyi Mountain National Park, China
Authors: Jin Xing , Lin Shu , Zhu Jing , Tan Fanglin , Zhang Huiguang , Chen Qichao , Hong Yu , Liu Jinfu , Xu Daowei , He Zhongsheng
Published: March 2024

Evaluating the carbon storage of forests and identifying the factors that influence it are essential in working towards the “dual carbon” goal. This assessment will facilitate research on carbon neutrality and promote regional ecological protection and development. This study utilized the “One Map” data of forest resources (2020) and the first year (2017) of the establishment of the national park in Wuyi Mountain National Park (WMNP). The continuous biomass expansion factor method, in conjunction with the vegetation carbon content coefficient, was employed to estimate the forest carbon storage within the park’s forested areas. Subsequently, the distribution of forest carbon storage was analyzed using remote sensing estimation methods, and a comparison was made between the forest carbon storage of these two years. Finally, correlation analysis and path analysis were conducted to identify the primary factors influencing forest carbon storage. The study findings reveal that in 2020, the total carbon storage in forests reached 4.851 × 106 t C, with an average carbon density of 49.55 t C·hm−2. Furthermore, the study identified positive correlations between dominant tree species, age groups, and elevation with carbon storage, whereas slope length and aspect were found to have negative correlations. Dominant tree species were observed to have the greatest impact on forest carbon storage in both 2017 and 2020, followed by age groups. These findings offer valuable scientific insights for the implementation of forest carbon storage pilot projects in WMNP.
The Impact of Quality Control Methods on Vegetation Monitoring Using MODIS FPAR Time Series
Authors: Yan Kai , Zhang Xingjian , Peng Rui , Gao Si , Liu Jinxiu
Published: March 2024

Monitoring vegetation dynamics (VD) is crucial for environmental protection, climate change research, and understanding carbon and water cycles. Remote sensing is an effective method for large-scale and long-term VD monitoring, but it faces challenges due to changing data uncertainties caused by various factors, including observational conditions. Previous studies have demonstrated the significance of implementing proper quality control (QC) of remote sensing data for accurate vegetation monitoring. However, the impact of different QC methods on VD results (magnitude and trend) has not been thoroughly studied. The fraction of absorbed photosynthetically active radiation (FPAR) characterizes the energy absorption capacity of the vegetation canopy and is widely used in VD monitoring. In this study, we investigated the effect of QC methods on vegetation monitoring using a 20-year MODIS FPAR time series. The results showed several important findings. Firstly, we observed that the Mixed-QC (no QC on the algorithm path) generally produced a lower average FPAR during the growing season compared to Main-QC (only using the main algorithm). Additionally, the Mixed-QC FPAR showed a very consistent interannual trend with the Main-QC FPAR over the period 2002–2021 (p < 0.05). Finally, we found that using only the main algorithm for QC generally reduced the trend magnitude (p < 0.1), particularly in forests. These results reveal differences in FPAR values between the two QC methods. However, the interannual FPAR trends demonstrate greater consistency. In conclusion, this study offers a case study on evaluating the influence of different QC methods on VD monitoring. It suggests that while different QC methods may result in different magnitudes of vegetation dynamics, their impact on the time series trends is limited.
Computational Models of Claudin Assembly in Tight Junctions and Strand Properties
Authors: McGuinness Sarah , Sajjadi Samaneh , Weber Christopher R. , Khalili-Araghi Fatemeh
Published: March 2024

Claudins are one of the major components of tight junctions (TJs) that polymerize within the cell membrane and form interactions between cells. Some claudins seal the paracellular space, limiting paracellular flux, while others form selectively permeable ion channels that control the paracellular permeability of small ions. Claudin strands are known to be dynamic and reshape within TJs to accommodate large-scale movements and rearrangements of epithelial tissues. Here, we summarize the recent computational and modeling studies on claudin assembly into tetrameric ion channels and their polymerization into μm long strands within the membrane. Computational studies ranging from all-atom molecular dynamics, coarse-grained simulations, and hybrid-resolution simulations elucidate the molecular nature of claudin assembly and function and provide a framework that describes the lateral flexibility of claudin strands.
A GASA Protein Family Gene, CmGEG, Inhibits Petal Growth in Chrysanthemum
Authors: He Ziying , Jiang Rui , Wang Xiaojing , Wang Yaqin
Published: March 2024

The diversity in the petal morphology of chrysanthemums makes this species an excellent model for investigating the regulation mechanisms of petal size. However, our understanding of the molecular regulation of petal growth in chrysanthemums remains limited. The GASA (gibberellic acid [GA]-stimulated Arabidopsis) protein plays a significant role in various aspects of plant growth and development. Previous studies have indicated that GEG (a gerbera homolog of the gibberellin-stimulated transcript 1 [GAST1] from tomato) is involved in regulating ray petal growth by inhibiting cell expansion in gerberas. In this study, we successfully cloned the GASA family gene from chrysanthemums, naming it CmGEG, which shares 81.4% homology with GEG. Our spatiotemporal expression analysis revealed that CmGEG is expressed in all tissues, with the highest expression levels observed in the ray florets, particularly during the later stages of development. Through transformation experiments, we demonstrated that CmGEG inhibits petal elongation in chrysanthemums. Further observations indicated that CmGEG restricts cell elongation in the top, middle, and basal regions of the petals. To investigate the relationship between CmGEG and GA in petal growth, we conducted a hormone treatment assay using detached chrysanthemum petals. Our results showed that GA promotes petal elongation while downregulating CmGEG expression. In conclusion, the constrained growth of chrysanthemum petals may be attributed to the inhibition of cell elongation by CmGEG, a process regulated by GA.
Gut Microbiome-Related Anti-Inflammatory Effects of Aryl Hydrocarbon Receptor Activation on Inflammatory Bowel Disease
Authors: Moutusy Salvinaz Islam , Ohsako Seiichiroh
Published: March 2024

Inflammatory bowel disease (IBD) is one of the most prevalent chronic inflammations of the gastrointestinal tract (GIT). The gut microbial population, the cytokine milieu, the aryl hydrocarbon receptor (AHR) expressed by immune and nonimmune cells and the intrinsic pathway of Th-cell differentiation are implicated in the immunopathology of IBD. AHR activation requires a delicate balance between regulatory and effector T-cells; loss of this balance can cause local gut microbial dysbiosis and intestinal inflammation. Thus, the study of the gut microbiome in association with AHR provides critical insights into IBD pathogenesis and interventions. This review will focus on the recent advancements to form conceptional frameworks on the benefits of AHR activation by commensal gut bacteria in IBD.
Molecular Modeling of the Multiple-Substrate Activity of the Human Recombinant Intra-Melanosomal Domain of Tyrosinase and Its OCA1B-Related Mutant Variant P406L
Authors: Dolinska Monika B. , Sergeev Yuri V.
Published: March 2024

Tyrosinase serves as the key enzyme in melanin biosynthesis, catalyzing the initial steps of the pathway, the hydroxylation of the amino acid L-tyrosine into L-3,4-dihydroxyphenylalanine (L-DOPA), followed by the subsequent oxidation of L-DOPA into dopaquinone (DQ), and it facilitates the conversion of 5,6-dihydroxyindole-2-carboxylic acid (DHICA) into 5,6-indolequinone-2-carboxylic acid (IQCA) and 5,6-dihydroxy indole (DHI) into indolequinone (IQ). Despite its versatile substrate capabilities, the precise mechanism underlying tyrosinase’s multi-substrate activity remains unclear. Previously, we expressed, purified, and characterized the recombinant intra-melanosomal domain of human tyrosinase (rTyr). Here, we demonstrate that rTyr mimics native human tyrosinase’s catalytic activities in vitro and in silico. Molecular docking and molecular dynamics (MD) simulations, based on rTyr’s homology model, reveal variable durability and binding preferences among tyrosinase substrates and products. Analysis of root mean square deviation (RMSD) highlights the significance of conserved residues (E203, K334, F347, and V377), which exhibit flexibility during the ligands’ binding. Additionally, in silico analysis demonstrated that the OCA1B-related P406L mutation in tyrosinase substantially influences substrate binding, as evidenced by the decreased number of stable ligand conformations. This correlation underscores the mutation’s impact on substrate docking, which aligns with the observed reduction in rTyr activity. Our study highlights how rTyr dynamically adjusts its structure to accommodate diverse substrates and suggests a way to modulate rTyr ligand plasticity.
Electrophoretic Deposition of Calcium Phosphates on Carbon–Carbon Composite Implants: Morphology, Phase/Chemical Composition and Biological Reactions
Authors: Skriabin Andrei S. , Tsygankov Petr A. , Vesnin Vladimir R. , Shakurov Alexey V. , Skriabina Elizaveta S. , Sviridova Irina K. , Sergeeva Natalia S. , Kirsanova Valentina A. , Akhmedova Suraya A. , Zherdeva Victoria V. , Lukina Yulia S. , Bionyshev-Abramov Leonid L.
Published: March 2024

Despite a long period of application of metal implants, carbon–carbon medical composites are also widely used for bone defect prosthesis in surgery, dentistry, and oncology. Such implants might demonstrate excellent mechanical properties, but their biocompatibility and integration efficiency into the host should be improved. As a method of enhancing, the electrophoretic deposition of fine-dispersed hydroxyapatite (HAp) on porous carbon substrates might be recommended. With electron microscopy, energy dispersion X-ray and Raman spectroscopy, and X-ray diffraction, we found that the deposition and subsequent heat post-treatment (up to the temperature of 400 °C for 1 h) did not lead to any significant phase and chemical transformations of raw non-stoichometric HAp. The Ca/P ratio was ≈1.51 in the coatings. Their non-toxicity, cyto- and biocompatibility were confirmed by in vitro and in vivo studies and no adverse reactions and side effects had been detected in the test. The proposed coating and subsequent heat treatment procedures provided improved biological responses in terms of resorption and biocompatibility had been confirmed by histological, magnetic resonance and X-ray tomographic ex vivo studies on the resected implant-containing biopsy samples from the BDF1 mouse model. The obtained results are expected to be useful for modern medical material science and clinical applications.
Raman Spectroscopy as a Research and Diagnostic Tool in Clinical Hematology and Hematooncology
Authors: Laskowska Paulina , Mrowka Piotr , Glodkowska-Mrowka Eliza
Published: March 2024

Raman spectroscopy is a molecular spectroscopic technique able to provide detailed information about the chemical structure, phase, crystallinity, and molecular interactions of virtually any analyzed sample. Although its medical applications have been studied for several decades, only recent advances in microscopy, lasers, detectors, and better understanding of the principles of the Raman effect have successfully expanded its applicability to clinical settings. The promise of a rapid, label-free diagnostic method able to evaluate the metabolic status of a cell in vivo makes Raman spectroscopy particularly attractive for hematology and oncology. Here, we review widely studied hematological applications of Raman spectroscopy such as leukocyte activation status, evaluation of treatment response, and differentiation between cancer and non-malignant cells, as well as its use in still unexplored areas in hematology. We also discuss limitations and challenges faced by Raman spectroscopy-based diagnostics as well as recent advances and modifications of the method aimed to increase its applicability to clinical hematooncology.
Genome-Wide Association Studies on the Kernel Row Number in a Multi-Parent Maize Population
Authors: Wang Yizhu , Ran Fengyun , Yin Xingfu , Jiang Fuyan , Bi Yaqi , Shaw Ranjan K. , Fan Xingming
Published: March 2024

Kernel row number (KRN) is a crucial trait in maize that directly influences yield; hence, understanding the mechanisms underlying KRN is vital for the development of high-yielding inbred lines and hybrids. We crossed four excellent panicle inbred lines (CML312, CML444, YML46, and YML32) with Ye107, and after eight generations of selfing, a multi-parent population was developed comprising four subpopulations, each consisting of 200 lines. KRN was accessed in five environments in Yunnan province over three years (2019, 2021, and 2022). The objectives of this study were to (1) identify quantitative trait loci and single nucleotide polymorphisms associated with KRN through linkage and genome-wide association analyses using high-quality genotypic data, (2) identify candidate genes regulating KRN by identifying co-localized QTLs and SNPs, and (3) explore the pathways involved in KRN formation and identify key candidate genes through Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. Our study successfully identified 277 significant Quantitative trait locus (QTLs) and 53 significant Single Nucleotide Polymorphism (SNPs) related to KRN. Based on gene expression, GO, and KEGG analyses, SNP-177304649, SNP-150393177, SNP-135283055, SNP-138554600, and SNP-120370778, which were highly likely to be associated with KRN, were identified. Seven novel candidate genes at this locus (Zm00001d022420, Zm00001d022421, Zm00001d016202, Zm00001d050984, Zm00001d050985, Zm00001d016000, and Zm00014a012929) are associated with KRN. Among these, Zm00014a012929 was identified using the reference genome Mo17. The remaining six genes were identified using the reference genome B73. To our knowledge, this is the first report on the association of these genes with KRN in maize. These findings provide a theoretical foundation and valuable insights into the genetic mechanisms underlying maize KRN and the development of high-yielding hybrids through heterosis.
Bioinformatics Insights on Viral Gene Expression Transactivation: From HIV-1 to SARS-CoV-2
Authors: Patarca Roberto , Haseltine William A.
Published: March 2024

Viruses provide vital insights into gene expression control. Viral transactivators, with other viral and cellular proteins, regulate expression of self, other viruses, and host genes with profound effects on infected cells, underlying inflammation, control of immune responses, and pathogenesis. The multifunctional Tat proteins of lentiviruses (HIV-1, HIV-2, and SIV) transactivate gene expression by recruiting host proteins and binding to transacting responsive regions (TARs) in viral and host RNAs. SARS-CoV-2 nucleocapsid participates in early viral transcription, recruits similar cellular proteins, and shares intracellular, surface, and extracellular distribution with Tat. SARS-CoV-2 nucleocapsid interacting with the replication–transcription complex might, therefore, transactivate viral and cellular RNAs in the transcription and reactivation of self and other viruses, acute and chronic pathogenesis, immune evasion, and viral evolution. Here, we show, by using primary and secondary structural comparisons, that the leaders of SARS-CoV-2 and other coronaviruses contain TAR-like sequences in stem-loops 2 and 3. The coronaviral nucleocapsid C-terminal domains harbor a region of similarity to TAR-binding regions of lentiviral Tat proteins, and coronaviral nonstructural protein 12 has a cysteine-rich metal binding, dimerization domain, as do lentiviral Tat proteins. Although SARS-CoV-1 nucleocapsid transactivated gene expression in a replicon-based study, further experimental evidence for coronaviral transactivation and its possible implications is warranted.
Protecting the Brain: Novel Strategies for Preventing Breast Cancer Brain Metastases through Selective Estrogen Receptor β Agonists and In Vitro Blood–Brain Barrier Models
Authors: Kirchner Janine , Völker Elisabeth , Shityakov Sergey , Saji Shigehira , Förster Carola Y.
Published: March 2024

Breast cancer brain metastasis (BCBM) is a challenging condition with limited treatment options and poor prognosis. Understanding the interactions between tumor cells and the blood–brain barrier (BBB) is critical for developing novel therapeutic strategies. One promising target is estrogen receptor β (ERβ), which promotes the expression of key tight junction proteins, sealing the BBB and reducing its permeability. In this study, we investigated the effects of 17β-estradiol (E2) and the selective ERβ agonist diarylpropionitrile (DPN) on endothelial and cancer cells. Western blot analysis revealed the expression patterns of ERs in these cell lines, and estrogen treatment upregulated claudin-5 expression in brain endothelial cells. Using in vitro models of the BBB, we found that DPN treatment significantly increased BBB tightness about suppressed BBB transmigration activity of representative Her2-positive (BT-474) and triple-negative (MDA-MB-231) breast cancer cell lines. However, the efficacy of DPN treatment decreased when cancer cells were pre-differentiated in the presence of E2. Our results support ERβ as a potential target for the prevention and treatment of BCBM and suggest that targeted vector-based approaches may be effective for future preventive and therapeutic implications.
Membrane Lipids and Osmolytes in the Response of the Acidophilic Basidiomycete Phlebiopsis gigantea to Heat, Cold, and Osmotic Shocks
Authors: Ianutsevich Elena A. , Danilova Olga A. , Grum-Grzhimaylo Olga A. , Tereshina Vera M.
Published: March 2024

Previously, we found for the first time the participation of osmolytes in adaptation to acidic conditions in three acidophilic fungi. Because trehalose can protect membranes, we hypothesized a relationship between osmolyte and membrane systems in adaptation to stressors. In the mycelium of Phlebiopsis gigantea, the level of osmolytes reaches 8% of the dry mass, while trehalose and arabitol make up 60% and 33% of the sum, respectively. Cold shock does not change the composition of osmolytes, heat shock causes a twofold increase in the trehalose level, and osmotic shock leads to a marked increase in the amount of trehalose and arabitol. Predominance of phospholipids (89% of the sum) and low proportions of sterols and sphingolipids are characteristic features of the membrane lipids’ composition. Phosphatidic acids, along with phosphatidylethanolamines and phosphatidylcholines, are the main membrane lipids. The composition of the membrane lipids remains constant under all shocks. The predominance of linoleic (75% of the sum) and palmitic (20%) acids in phospholipids results in a high degree of unsaturation (1.5). Minor fluctuations in the fatty acid composition are observed under all shocks. The results demonstrate that maintaining or increasing the trehalose level provides stability in the membrane lipid composition during adaptation.
Toxicogenomics of the Freshwater Oligochaete, Tubifex tubifex (Annelida, Clitellata), in Acute Water-Only Exposure to Arsenic
Authors: Moreno-Ocio Iñigo , Aquilino Mónica , Llorente Lola , Martínez-Madrid Maite , Rodríguez Pilar , Méndez-Fernández Leire , Planelló Rosario
Published: March 2024

A toxicogenomic approach was used for toxicity evaluation of arsenic in the aquatic environment, and differential gene expression was investigated from 24 h and 96 h water-only acute toxicity tests with the aquatic oligochaete, Tubifex tubifex (Annelida, Clitellata). Several toxicological endpoints (survival and autotomy) of the oligochaete and tissue residues were measured, and dose-response modelling of gene expression data was studied. A reference transcriptome of the aquatic oligochaete, T. tubifex, was reconstructed for the first time, and genes related to cell stress response (Hsc70, Hsp10, Hsp60, and Hsp83), energy metabolism (COX1), oxidative stress (Cat, GSR, and MnSOD), and the genes involved in the homeostasis of organisms (CaM, RpS13, and UBE2) were identified and characterised. The potential use of the genes identified for risk assessment in freshwater ecosystems as early biomarkers of arsenic toxicity is discussed.
Qualitative and Quantitative Analytical Techniques of Nucleic Acid Modification Based on Mass Spectrometry for Biomarker Discovery
Authors: Liu Ying , Dong Jia-Hui , Shen Xu-Yang , Gu Yi-Xuan , Zhang Run-Hong , Cui Ruo-Yao , Liu Ya-Hong , Zhou Jiang , Zhou Ying-Lin , Zhang Xin-Xiang
Published: March 2024

Nucleic acid modifications play important roles in biological activities and disease occurrences, and have been considered as cancer biomarkers. Due to the relatively low amount of nucleic acid modifications in biological samples, it is necessary to develop sensitive and reliable qualitative and quantitative methods to reveal the content of any modifications. In this review, the key processes affecting the qualitative and quantitative analyses are discussed, such as sample digestion, nucleoside extraction, chemical labeling, chromatographic separation, mass spectrometry detection, and data processing. The improvement of the detection sensitivity and specificity of analytical methods based on mass spectrometry makes it possible to study low-abundance modifications and their biological functions. Some typical nucleic acid modifications and their potential as biomarkers are displayed, and efforts to improve diagnostic accuracy are discussed. Future perspectives are raised for this research field.
Functional Consequences of CFTR Interactions in Cystic Fibrosis
Authors: Ramananda Yashaswini , Naren Anjaparavanda P. , Arora Kavisha
Published: March 2024

Cystic fibrosis (CF) is a fatal autosomal recessive disorder caused by the loss of function mutations within a single gene for the Cystic Fibrosis Transmembrane Conductance Regulator (CFTR). CFTR is a chloride channel that regulates ion and fluid transport across various epithelia. The discovery of CFTR as the CF gene and its cloning in 1989, coupled with extensive research that went into the understanding of the underlying biological mechanisms of CF, have led to the development of revolutionary therapies in CF that we see today. The highly effective modulator therapies have increased the survival rates of CF patients and shifted the epidemiological landscape and disease prognosis. However, the differential effect of modulators among CF patients and the presence of non-responders and ineligible patients underscore the need to develop specialized and customized therapies for a significant number of patients. Recent advances in the understanding of the CFTR structure, its expression, and defined cellular compositions will aid in developing more precise therapies. As the lifespan of CF patients continues to increase, it is becoming critical to clinically address the extra-pulmonary manifestations of CF disease to improve the quality of life of the patients. In-depth analysis of the molecular signature of different CF organs at the transcriptional and post-transcriptional levels is rapidly advancing and will help address the etiological causes and variability of CF among patients and develop precision medicine in CF. In this review, we will provide an overview of CF disease, leading to the discovery and characterization of CFTR and the development of CFTR modulators. The later sections of the review will delve into the key findings derived from single-molecule and single-cell-level analyses of CFTR, followed by an exploration of disease-relevant protein complexes of CFTR that may ultimately define the etiological course of CF disease.