bluuebunny/crossref_metadata_2025 huggingface dataset:
What Is the Optimal Solution for Scheduling Multiple Energy Systems? Overview and Analysis of Integrated Energy Co-Dispatch Models
Authors: Gao Xiaozhi , Xiao Han , Xu Shiwei , Lin Hsiung-Cheng , Chang Pengyu
Published: September 2024

With increasing dual pressure from global large energy consumption and environmental protection, multiple integrated energy systems (IESs) can provide more effective ways to achieve better energy utilization performance. However, in actual circumstances, many challenges have been brought to coupling multiple energy sources along with the uncertainty of each generated power to achieve efficient operation of IESs. To resolve this problem, this article reviews primary research on integrated energy optimization and scheduling technology to give constructive guidance in power systems. Firstly, the conceptual composition and classification of IESs are presented. Secondly, the coupling relationship between multiple energy sources based on mathematical expression is studied deeply. Thirdly, the scheduling of IESs with different types and regions is classified, analyzed, and summarized for clarification. Fourthly, on this basis, potential solutions for applications of key optimization technologies involved in the scheduling process in IESs can be found systematically. Finally, the future development trends to optimize scheduling integrated energy systems is explored and prospected in depth.
Total Ionizing Dose and Single-Event Effect Response of the AD524CDZ Instrumentation Amplifier
Authors: Cardenas Chavez Jaime , Hiemstra Dave , Noguera Cundar Adriana , Johnson Brayden , Baik David , Chen Li
Published: September 2024

This manuscript focuses on studying the radiation response of the Commercial-off-the-shelf (COTS) AD524CDZ operational amplifier. Total Ionizing Dose (TID) effects were tested using low-dose 60Co irradiation. Single-Event Effect (SEE) sensitivity was studied on this operational amplifier using a 105 MeV proton beam. Additionally, further study of the SEE response was carried out using a Two-photon absorption laser to scan some sensitive sectors of the die. For this laser experiment, different gain setups and laser energies were employed to determine how the Single Event Transient (SET) response of the device was affected based on the test configuration. The results from the TID experiments revealed that the studied device remained functional after 100 krads (Si). Proton experiments revealed the studied device exhibited a high SET response with a maximum DC offset SET of about 1.5 V. Laser experiments demonstrated that there was a clear SET reduction when using 10× and 1000× gain setups.
A Flyback Converter with a Simple Passive Circuit for Improving Power Efficiency
Authors: Choi Woo-Young
Published: September 2024

This paper proposes an effective method to improve the power efficiency of the flyback converter in the continuous conduction mode (CCM). The proposed converter uses a simple passive circuit to reduce the switching power losses. The current through the output diode can be shifted to a new branch where one diode, one inductor, and one auxiliary winding of the transformer are included. The output diode current can be reduced to zero for the zero-current switching of the output diode. The additional inductor is used to control the changing rate of the additional diode current to reduce the reverse-recovery current. Keeping the simplicity of the passive method, the proposed converter improves the power efficiency compared to the conventional converter. The circuit configuration and the operation principle are described. The design considerations are presented, including the simulation verification. The experimental results for a 45 W prototype are discussed to evaluate the performance of the proposed converter. The proposed converter has achieved a power efficiency of 93.5% for the rated load condition, improving the power efficiency. The applications of the proposed converter are also discussed for the future research directions.
Continuously Learning Prediction Models for Smart Domestic Hot Water Management
Authors: Bayle Raphaël , Reyboz Marina , Lomet Aurore , Cook Victor , Mermillod Martial
Published: September 2024

Domestic hot water (DHW) consumption represents a significant portion of household energy usage, prompting the exploration of smart heat pump technology to efficiently meet DHW demands while minimizing energy waste. This paper proposes an innovative investigation of models using deep learning and continual learning algorithms to personalize DHW predictions of household occupants’ behavior. Such models, alongside a control system that decides when to heat, enable the development of a heat-pumped-based smart DHW production system, which can heat water only when needed and avoid energy loss due to the storage of hot water. Deep learning models, and attention-based models particularly, can be used to predict time series efficiently. However, they suffer from catastrophic forgetting, meaning that when they dynamically learn new patterns, older ones tend to be quickly forgotten. In this work, the continuous learning of DHW consumption prediction has been addressed by benchmarking proven continual learning methods on both real dwelling and synthetic DHW consumption data. Task-per-task analysis reveals, among the data from real dwellings that do not present explicit distribution changes, a gain compared to the non-evolutive model. Our experiment with synthetic data confirms that continual learning methods improve prediction performance.
Ultra-Short-Term Photovoltaic Power Prediction by NRGA-BiLSTM Considering Seasonality and Periodicity of Data
Authors: Wu Hong , Liu Haipeng , Jin Huaiping , He Yanping
Published: September 2024

Photovoltaic (PV) power generation is highly stochastic and intermittent, which poses a challenge to the planning and operation of existing power systems. To enhance the accuracy of PV power prediction and ensure the safe operation of the power system, a novel approach based on seasonal division and a periodic attention mechanism (PAM) for PV power prediction is proposed. First, the dataset is divided into three components of trend, period, and residual under fuzzy c-means clustering (FCM) and the seasonal decomposition (SD) method according to four seasons. Three independent bidirectional long short-term memory (BiLTSM) networks are constructed for these subsequences. Then, the network is optimized using the improved Newton–Raphson genetic algorithm (NRGA), and the innovative PAM is added to focus on the periodic characteristics of the data. Finally, the results of each component are summarized to obtain the final prediction results. A case study of the Australian DKASC Alice Spring PV power plant dataset demonstrates the performance of the proposed approach. Compared with other paper models, the MAE, RMSE, and MAPE performance evaluation indexes show that the proposed approach has excellent performance in predicting output power accuracy and stability.
Assessment of the Profitability of a Photovoltaic Installation Cooperating with Energy Storage Using an Example of a Medium-Sized Production Company
Authors: Mikulik Jerzy , Niekurzak Mariusz
Published: September 2024

This work aims to comprehensively analyze the cooperation of an electricity storage facility with an operating photovoltaic installation in a manufacturing company regarding the efficiency and effectiveness of the device and the economic profitability of the investment. This work aims to check the benefits that can be brought by expanding the PV system with an electricity storage facility. Based on the real energy balance and the characteristics of electricity distribution in the company, profitability calculations were carried out reflecting the expected savings generated by using individual solutions. These methods allowed the authors to calculate the market value of the investment with the assumed boundary criteria and to determine the economic effectiveness of the investment. Additionally, the practical process of selecting an electricity storage facility was presented and key moments in the company’s energy report were analyzed, in which the use of a battery could bring results. Calculations showed that supplementing the described PV installation with an energy storage facility will increase the current level of self-consumption of PV energy by over 14%. The benefits translate into the final effect of energy storage operation, which brings additional annual savings for the company of approximately EUR 23,000 in the case of a weaker device and roughly EUR 40,000 in the case of a more powerful energy storage device. The proposed research could improve the planning of new industrial plants for photovoltaic installations, as well as the redesign of existing ones.
A Single-Phase Ground Fault Line Selection Method in Active Distribution Networks Based on Transformer Grounding Mode Modification
Authors: Su Shi , Xie Qingyang , Ma Pengfei , Li Yuan , Chen Fahui , Zhang Jing , Li Botong , Wang Changqi
Published: September 2024

Reliable fault line selection technology is crucial for preventing fault range expansion and ensuring the reliable operation of distribution networks. Modern distribution systems with neutral earthing via arc extinguishing coil face challenges during single-phase ground faults due to indistinct fault characteristics and system sequence networks influenced by the grounding methods on the distributed generation side. These factors increase the difficulty of fault line selection. By analyzing the differences between the zero-sequence currents of feeder lines and neutral lines in active distribution networks with neutral earthing via arc extinguishing coil, a method for single-phase ground fault line selection has been proposed in this paper. This method involves switching from a neutral point ungrounded mode to a low-resistance neutral grounding mode using distributed generation grid-connected transformers under permanent fault conditions. Criteria based on the differences in zero-sequence current ratios before and after the grounding mode switch are established. Simulation validation using the Power Systems Computer Aided Design (PSCAD) platform has been conducted. The proposed method demonstrates strong tolerance to transition resistance, simple extraction of fault characteristic signals, and accurate fault line selection results.
Study on Fast Temporal Prediction Method of Flame Propagation Velocity in Methane Gas Deflagration Experiment Based on Neural Network
Authors: Wang Xueqi , Wang Boqiao , Yu Kuai , Zhu Wenbin , Zhang Jinnan , Zhang Bin
Published: September 2024

To address the challenges of high experimental costs, complexity, and time consumption associated with pre-mixed combustible gas deflagration experiments under semi-open space obstacle conditions, a rapid temporal prediction method for flame propagation velocity based on Ranger-GRU neural networks is proposed. The deflagration experiment data are employed as the training dataset for the neural network, with the coefficient of determination (R2) and mean squared error (MSE) used as evaluation metrics to assess the predictive performance of the network. First, 108 sets of pre-mixed methane gas deflagration experiments were conducted, varying obstacle parameters to investigate methane deflagration mechanisms under different conditions. The experimental results demonstrate that obstacle-to-ignition source distance, obstacle shape, obstacle length, obstacle quantity, and thick and fine wire mesh obstacles all significantly influence flame propagation velocity. Subsequently, the GRU neural network was trained, and different activation functions (Sigmoid, Relu, PReLU) and optimizers (Lookahead, RAdam, Adam, Ranger) were incorporated into the backpropagation updating process of the network. The training results show that the Ranger-GRU neural network based on the PReLU activation function achieves the highest mean R2 value of 0.96 and the lowest mean MSE value of 7.16759. Therefore, the Ranger-GRU neural network with PReLU activation function can be a viable rapid prediction method for flame propagation velocity in pre-mixed methane gas deflagration experiments under semi-open space obstacle conditions.
Correction: Shi et al. Parameter Estimation Method for Virtual Power Plant Frequency Response Model Based on SLP. Energies 2024, 17, 3124
Authors: Shi Zheng , Zhu Haixiao , Zhao Haibo , Wang Peng , Liang Yan , Wang Kaikai , Chen Jie , Zheng Xiaoming , Liu Hongli
Published: September 2024

In the original publication [...]
The Possibility of Using Waste from Dye Sorption for Methane Production
Authors: Nowicka Anna , Jóźwiak Tomasz , Zieliński Marcin
Published: September 2024

The aim of this study was to determine the effect of sorption of Basic Red 46 (BR46) dye by lignocellulosic biomass on the susceptibility of the sorbed waste to anaerobic decomposition by anaerobic digestion. The research material used in the experiment consisted of two types of biomass: stalks with leaves and inflorescences after mowing Canadian goldenrod (Solidago canadensis L.) (GB), and rapeseed hulls (RHs) after oil pressing. During the anaerobic decomposition of RHs, 732.30 NmL/gVS and 646.63 NmL/gVS of methane were obtained from the non-sorbed substrate and the plant material after dye sorption, respectively. Similarly, in the variants using Canadian goldenrod, the production was 220.70 NmL/gVS and 183.20 NmL/gVS. The GB sorbent sorbed 34% more BR46 dye than the RH sorbent, which is likely to have resulted in the accumulation of VFA and contributed to the partial inhibition of methane production. In light of the obtained results and the literature data, it is concluded that there is a possibility of effective use of dye sorption waste for methane production.
Coordinated Control of Transient Voltage Support in Doubly Fed Induction Generators
Authors: Xu Guanghu , Qiu Jian , Zhang Jianxin , Yang Huanhuan , Gao Qin , Jiang Tuo , Wang Yuan
Published: September 2024

The large-scale integration of wind power significantly alters the voltage dynamic characteristics of power systems. Wind turbines have a weak ability to withstand grid disturbances and have difficulty in providing effective reactive power support during transient periods. The sensitivity of wind turbines to the grid voltage significantly increases the probability of large-scale, cascading off-grid events. This paper proposes a coordinated control strategy to enhance the transient reactive power support capability of doubly fed wind farms. The additional stator current demagnetization control reduces the risk of a crowbar protection action after a fault and ensures that the unit power is controllable. Based on the voltage–reactive power coupling relationship, each unit can produce reactive power according to the voltage–reactive power sensitivity matrix during the transient period. After the reactive power output of the unit reaches the limit, transient active and reactive combined control is further adopted to reduce the active power output of the unit to a certain extent and improve the reactive power support capability. Finally, two cases are built in the PSCAD to verify the effectiveness of the proposed control strategy. The results show that the proposed control strategy can enable the wind farm to output more reactive power to the grid during the transient period, effectively supporting the system voltage during the transient process and avoiding further deterioration of the fault.
A Defect Detection Method for Grading Rings of Transmission Lines Based on Improved YOLOv8
Authors: Xiang Siyu , Zhang Linghao , Chen Yumin , Du Peike , Wang Yao , Xi Yue , Li Bing , Zhao Zhenbing
Published: September 2024

Detecting defects in aerial images of grading rings collected by drones poses challenges due to the structural similarity between normal and defective samples. The small visual differences make it hard to distinguish defects and extract key features. Additionally, critical defect features often become lost during feature fusion. To address these issues, this paper uses YOLOv8 as the baseline model and proposes an improved YOLOv8-based method for detecting grading ring defects in transmission lines. Our approach first integrates the CloAttention and C2f modules into the feature extraction network, enhancing the model’s ability to capture and identify defect features in grading rings. Additionally, we incorporate CARAFE into the feature fusion network to replace the original upsampling module, effectively reducing the loss of critical defect information during the fusion process. Experimental results demonstrate that our method achieves an average detection accuracy of 67.6% for grading ring defects, marking a 6.8% improvement over the baseline model. This improvement significantly enhances the effectiveness of defect detection in transmission line grading rings.
Portrait of the Decarbonization and Renewables Penetration in Oman’s Energy Mix, Motivated by Oman’s National Green Hydrogen Plan
Authors: Marzouk Osama A.
Published: September 2024

The aim of this study is to quantitatively describe the anticipated change in the energy mix of the Sultanate of Oman (Oman) as the country moves forward in its national plan for green hydrogen, in order to become a global producer and exporter. This aim is achieved by curating recent data about energy projects in Oman that are either operating or planned (in a construction or pre-construction stage). Then, these data are processed further to extract useful insights about how the energy mix would change if the planned projects are realized and added to the operating ones. This reveals the serious commitment of the country to accomplish its national plan for green hydrogen (GH), where the green hydrogen production ambition for 2030 is about 1.125 million tons per annum (Mtpa), using a renewable energy capacity of approximately 18 GW. This ambition increases to about 3.5 Mtpa with approximately 70 GW of renewables in 2040, and increases further to about 8 Mtpa with approximately 180 GW of renewables in 2050. As a portrait of Oman’s energy mix with the assumption of successfully completing all planned energy projects, we found that the country is expected to have a total capacity of 83.1271 GW, with the share of renewables (solar and wind) reaching 83.133% (as compared to 15.0711 GW with an 8.907% renewables share for operating projects). Nearly all (precisely 99.571%) of the 68.0560 GW planned national energy capacity additions are based on solar or wind energy, while the traditional oil–gas energy is gradually phased out. Green hydrogen production dominates this surge in renewables penetration within the Omani energy mix, with 84.659% of the planned 34.3140 GW solar capacity additions linked with green hydrogen production, for operating water electrolyzers. Similarly, 98.804% of the planned 33.4500 GW wind capacity additions are linked with green hydrogen production.
Experimental Study on Heat Recovery in a CaO/Ca(OH)2-Based Mechanical Fluidized Bed Thermochemical Energy Storage Reactor
Authors: Kühl Viktor , Linder Marc , Schmidt Matthias
Published: September 2024

Long-term storage of seasonally available solar energy and its provision to balance heating energy demand can contribute significantly to the sustainable use of energy resources. Thermochemical energy storage is a suitable process for this purpose, offering the possibility of loss-free long-term energy storing and heat supply. In order to develop suitable technical solutions for the use of this technology, novel reactor concepts and scientific questions regarding material and technology development are being investigated. In this publication, the energy storage process of a long-term energy storage system based on a ploughshare reactor is experimentally investigated under various technically relevant operating conditions. One specific aspect of this technology is related to the release of water vapour during the charging process. Therefore, this work focusses, in particular, on the possibility of technically utilizing the latent heat of the released water vapour in the range of 45 °C to 80 °C, which covers the operating requirements of common heating systems in households. The experiments have shown that the dehydration process enables the separation of two heat fluxes: the chemically bound energy for long-term storage and the physically (sensible and latent) stored energy for short-term applications. However, the limitation of gas transport was also identified as the most important influencing parameter for optimising the performance of the process.
Considering Differentiated Pricing Mechanisms for Multiple Power Levels
Authors: Li Lili , Yin Qingyu , Liu Xiaonan , Zu Guoqiang
Published: September 2024

With the development of supercharging technology, charging stations will face supercharging piles and fast-charging piles coexisting for a long time. The traditional unified service fee pricing model will face the problem of slow-charging vehicles occupying supercharging piles, and the advantages of supercharging facilities cannot be fully utilized. Considering factors such as station revenue, station utilization rate, and user willingness, this paper constructs a differentiated pricing model for multi-power-level charging facilities based on capacity and electricity service fees. It reveals the impact of this pricing model on the economy and user satisfaction of charging stations. In addition, a multi-objective optimization model is constructed and solved by a multi-objective particle swarm method to determine the pricing strategy of different power levels that adapt to the actual situation. The results show that the differentiated pricing mechanism can increase the station revenue by about 10.74% and the station stack power utilization rate by about 7.14%, and user satisfaction is higher, which is better than the conventional pricing mechanism. Therefore, the proposed charging pricing strategy can provide a decision-making reference for charging station operation and planning.
Effect of Meteorological Variables on Energy Demand in the Northeast and Southeast Regions of Brazil
Authors: Gomes Helber Barros , Herdies Dirceu Luís , Santos Luiz Fernando dos , Hackerott João Augusto , Herdies Bruno Ribeiro , Silva Fabrício Daniel dos Santos , Silva Maria Cristina Lemos da , de Quadro Mario Francisco Leal , Semolini Robinson , Cortez Amanda , Schatz Bruna , Cerqueira Bruno Dantas , Moura Junior Djanilton Henrique
Published: September 2024

Energy consumption demand has shown successive records during recent months, primarily associated with heat waves in almost all Brazilian states. The effects of climate change induced by global warming and the increasingly frequent occurrence of extreme events, mainly regarding temperature and precipitation, are associated with this increase in demand. In this sense, the impact of meteorological variables on load demand in some substations in the northeast and southeast of Brazil was analyzed, considering the historical series of energy injected into these substations. Fifteen substations were analyzed: three in the state of São Paulo, six in Bahia, three in Pernambuco, and three in Rio Grande do Norte. Initially, essential quality control was carried out on the energy injection data. The SAMeT data sets were used for the variable temperature, and Xavier was used for precipitation and relative humidity to obtain a homogeneous data series. Daily and monthly data were used for a detailed analysis of these variables in energy demand over the northeast and southeast regions of Brazil. Some regions were observed to be sensitive to the maximum temperature variable, while others were sensitive to the average temperature. On the other hand, few cases showed the highest correlation with the precipitation and relative humidity variables, with most cases being considered slight or close to zero. A more refined analysis was based on the type of consumers associated with each substation. These results showed that where consumption is more residential, the highest correlations were associated with maximum temperature in most stations in the northeast and average temperature in the southeast. In regions where consumption is primarily rural, the correlation observed with precipitation and relative humidity was the highest despite being negative. A more detailed analysis shows that rural production is associated with irrigation in these substations, which partly explains consumption, as when rainfall occurs, the demand for irrigation decreases, and thus energy consumption is reduced.
Navigating Urban Sustainability: Urban Planning and the Predictive Analysis of Busan’s Green Area Dynamics Using the CA-ANN Model
Authors: Park Minkyu , Lee Jaekyung , Won Jongho
Published: September 2024

While numerous studies have employed deep learning and high-resolution remote sensing to predict future land use and land cover (LULC) changes, no study has integrated these predictive tools with the current urban planning context to find a potential issues for sustainability. This study addresses this gap by examining the planning context of Busan Metropolitan City (BMC) and analyzing the paradoxical objectives within the city’s 2040 Master Plan and the subordinate 2030 Busan Master Plan for Parks and Greenbelts. Although the plans advocate for increased green areas to enhance urban sustainability and social wellbeing, they simultaneously support policies that may lead to a reduction in these areas due to urban development. Using the CA-ANN model in the MOLUSCE plugin, a deep learning-based LULC change analysis, we forecast further urban expansion and continued shrinkage of natural green areas. During 1980–2010, Busan Metropolitan City (BMC) underwent high-speed urban expansion, wherein the urbanized areas almost doubled and agricultural lands and green areas, including forests and grassland, reduced considerably. Forecasts for the years 2010–2040 show continued further expansion of urban areas at the expense of areas for agriculture and green areas, including forest and grasslands. Given the master plans, these highlight a critical tension between urban growth and sustainability. Despite the push for more green spaces, the replacement of natural landscapes with artificial parks and green areas may threaten long-term sustainability. In view of these apparently conflicting goals, the urban planning framework for BMC would have to take up increasingly stronger conservation policies and adaptive planning practices that consider environmental preservation on a par with economic development in the light of the planning context and trajectory of urbanization.
Research Progress of Machine Learning in Extending and Regulating the Shelf Life of Fruits and Vegetables
Authors: Li Dawei , Bai Lin , Wang Rong , Ying Sun
Published: September 2024

Fruits and vegetables are valued for their flavor and high nutritional content, but their perishability and seasonality present challenges for storage and marketing. To address these, it is essential to accurately monitor their quality and predict shelf life. Unlike traditional methods, machine learning efficiently handles large datasets, identifies complex patterns, and builds predictive models to estimate food shelf life. These models can be continuously refined with new data, improving accuracy and robustness over time. This article discusses key machine learning methods for predicting shelf life and quality control of fruits and vegetables, with a focus on storage conditions, physicochemical properties, and non-destructive testing. It emphasizes advances such as dataset expansion, model optimization, multi-model fusion, and integration of deep learning and non-destructive testing. These developments aim to reduce resource waste, provide theoretical basis and technical guidance for the formation of modern intelligent agricultural supply chains, promote sustainable green development of the food industry, and foster interdisciplinary integration in the field of artificial intelligence.
The Clinical Spectrum of Mosaic Genetic Disease
Authors: Geiger Hanabi , Furuta Yutaka , van Wyk Suné , Phillips John A. , Tinker Rory J.
Published: September 2024

Genetic mosaicism is defined as the presence of two or more cell lineages with different genotypes arising from a single zygote. Mosaicism has been implicated in hundreds of genetic diseases with diverse genetic etiologies affecting every organ system. Mosaic genetic disease (MDG) is a spectrum that, on the extreme ends, enables survival from genetic severe disorders that would be lethal in a non-mosaic form. On the milder end of the spectrum, mosaicism can result in little if any phenotypic effects but increases the risk of transmitting a pathogenic genotype. In the middle of the spectrum, mosaicism has been implicated in reducing the phenotypic severity of genetic disease. In this review will describe the spectrum of mosaic genetic disease whilst discussing the status of the detection and prevalence of mosaic genetic disease.
Neuroticism as a Common Factor in Depression and Anxiety Associated with Multiple Sclerosis—A Systematic Review and Meta-Analysis
Authors: Schenk Alina , Popa Cosmin Octavian , Cojocaru Cristiana Manuela , Marian Ștefan , Maier Smaranda , Bălașa Rodica
Published: September 2024

Background: Left undiagnosed and untreated, the association between multiple sclerosis and mental health difficulties significantly increases the multimorbidity risk in these patients. Hence, the purpose of this systematic review and meta-analysis was to estimate the prevalence of neuroticism, depression, and anxiety in MS and to explore the cumulative impact of these psychological factors on the disease expression. Methods: A literature search was conducted on PubMed, Web of Science, Scopus, and Google Scholar databases, according to the PRISMA guidelines. Also, the potential risk of bias was assessed using the AXIS tool. Result: After a rigorous full-text examination, among the 756 identified studies, 22 investigations were considered for the systematic review, and 10 studies were selected for the meta-analysis. The prevalence of neuroticism in the studied population was 24.06% (95% CI: 16.79–33.34), of depression 20.77% (95% CI: 7.67–33.88), while the presence of anxiety was found in 23.94% (95% CI: 6.21–40.36). Conclusions: The main finding of this research confirms that psychiatric disorders often co-occur with MS, impacting the clinical symptoms and life quality of patients living with this illness. For a better understanding of the interaction between personality, depression, anxiety, and the disease symptoms, future research should consider conducting comparisons on more homogenous studies.
Enhancing Agricultural Productivity: Integrating Remote Sensing Techniques for Cotton Yield Monitoring and Assessment
Authors: Aghayev Amil , Řezník Tomáš , Konečný Milan
Published: September 2024

This study assesses soil productivity in a 15-hectare cotton field using an integrated approach combining field data, laboratory analysis, and remote sensing techniques. Soil samples were collected and analyzed for key parameters including nitrogen (N), humus, phosphorus (P2O5), potassium (K2O), carbonates, pH, and electrical conductivity (EC). In addition to low salinity, these analyses showed low results for humus and nutrient parameters. A Pearson correlation analysis showed that low organic matter and high salinity had a strong negative correlation with crop productivity, explaining 37% of the variation in NDVI values. Remote sensing indices (NDVI, SAVI, NDMI, and NDSI) confirmed these findings by highlighting the relationship between soil properties and spectral reflectance. This research demonstrates the effectiveness of remote sensing in soil assessment, emphasizing its critical role in sustainable agricultural planning. By integrating traditional methods with advanced remote sensing technologies, this study provides actionable insights for policymakers and practitioners to improve soil productivity and ensure food security.
Discovery and Prediction Study of the Dominant Pharmacological Action Organ of Aconitum carmichaeli Debeaux Using Multiple Bioinformatic Analyses
Authors: Park Musun , Seo Eun-Hye , Yi Jin-Mu , Cha Seongwon
Published: September 2024

Herbs, such as Aconitum carmichaeli Debeaux (ACD), have long been used as therapies, but it is difficult to identify which organs of the human body are affected by the various compounds. In this study, we predicted the organ where the drug predominantly acts using bioinformatics and verified it using transcriptomics. We constructed a computer-aided brain system network (BSN) and intestinal system network (ISN). We predicted the action points of ACD using network pharmacology (NP) analysis and predicted the dockable proteins acting in the BSN and ISN using statistical-based docking analysis. The predicted results were verified using ACD-induced transcriptome analysis. The predicted results showed that both the NP and docking analyses predominantly acted on the BSN and showed better hit rates in the hub nodes. In addition, we confirmed through verification experiments that the SW1783 cell line had more than 10 times more differentially expressed genes than the HT29 cell line and that the dominant acting organ is the brain, using network dimension spanning analysis. In conclusion, we found that ACD preferentially acts in the brain rather than in the intestine, and this multi-bioinformatics-based approach is expected to be used in future studies of drug efficacy and side effects.
Association of the rs9896052 Polymorphism Upstream of GRB2 with Proliferative Diabetic Retinopathy in Patients with Less than 10 Years of Diabetes
Authors: Bastos Caroline Moura Cardoso , da Silva Machado Lucas Marcelo , Crispim Daisy , Canani Luís Henrique , dos Santos Kátia Gonçalves
Published: September 2024

Growth factor receptor-bound protein 2 (GRB2) is a negative regulator of insulin signaling and a positive regulator of angiogenesis. Its expression is increased in a mouse model of retinal neovascularization and in patients with type 2 diabetes mellitus (T2DM). This case–control study aimed to investigate the association between the rs9896052 polymorphism (A>C) upstream of GRB2 and proliferative diabetic retinopathy (PDR) in patients with T2DM from Southern Brazil, taking into consideration self-reported skin color (white or non-white) and the known duration of diabetes (<10 years or ≥10 years). Genotypes were determined by real-time PCR in 838 patients with T2DM (284 cases with PDR and 554 controls without DR). In the total study group and in the analysis stratified by skin color, the genotype and allele frequencies were similar between cases and controls. However, among patients with less than 10 years of diabetes, the C allele was more frequent in cases than in controls (63.3% versus 51.8%, p = 0.032), and the CC genotype was independently associated with an increased risk of PDR (adjusted OR = 2.82, 95% CI 1.17–6.75). In conclusion, our findings support the hypothesis that the rs9896052 polymorphism near GRB2 is associated with PDR in Brazilian patients with T2DM.
Microalgae Flocculation: Assessment of Extraction Yields and Biological Activity
Authors: Imbimbo Paola , Ferrara Alfonso , Giustino Enrica , Liberti Davide , Monti Daria Maria
Published: September 2024

Downstream costs represent one of the main obstacles to enabling microalgae to become widespread. The development of an economical, easily scaled-up strategy could reduce the overall process costs. Here, different flocculants were tested on different microalgae strains and a cyanobacterium. The results indicate that flocculation could be an alternative to centrifugation, as CaCl2 induced a complete flocculation of green and red marine strains (96 ± 4% and 87.0 ± 0.5%, respectively), whereas Chitosan was the only agent able to induce flocculation on the cyanobacterium (46 ± 1%). As for the thermoacidophilic red microalga, 100% flocculation was achieved only by increasing the pH. Carotenoids were extracted from the flocculated biomass, and the strategy improved with the use of the wet biomass. The results indicate that flocculation does not affect carotenoid yield, which is at least the same than that obtained upon centrifugation and extraction from the wet biomass. Then, for the first time, the biological activity of the extracts obtained from the flocculated biomasses was evaluated. The results indicate that only the green microalga extract shows increased antioxidant activity. In conclusion, this work highlights that a general downstream procedure cannot be developed for microalgae strains but should be rationally tailored.
Defenders of the Transcriptome: Guard Protein-Mediated mRNA Quality Control in Saccharomyces cerevisiae
Authors: Querl Luisa , Krebber Heike
Published: September 2024

Cell survival depends on precise gene expression, which is controlled sequentially. The guard proteins surveil mRNAs from their synthesis in the nucleus to their translation in the cytoplasm. Although the proteins within this group share many similarities, they play distinct roles in controlling nuclear mRNA maturation and cytoplasmic translation by supporting the degradation of faulty transcripts. Notably, this group is continuously expanding, currently including the RNA-binding proteins Npl3, Gbp2, Hrb1, Hrp1, and Nab2 in Saccharomyces cerevisiae. Some of the human serine–arginine (SR) splicing factors (SRSFs) show remarkable similarities to the yeast guard proteins and may be considered as functional homologues. Here, we provide a comprehensive summary of their crucial mRNA surveillance functions and their implications for cellular health.
The Potential Related Genes and Mechanisms Involved in Improving the Treadmill Exercise Ability of APP/PS1 Mice
Authors: Zhao Zhe , Wu Xingqing , Wu Weijia , Tang Yingzhe , Meng Xiangyuan , Peng Mei , Tang Changfa , Zheng Lan , Liu Wenfeng
Published: September 2024

Alzheimer’s disease (AD) causes a decline in skeletal muscle function, which can further exacerbate the cognitive dysfunction of patients with AD. It has been widely established that exercise improves AD brain pathology, but the role of skeletal muscle in AD is still poorly understood. In this study, we investigated the effects of treadmill exercise on the exercise ability of APP/PS1 transgenic AD mice and explored potential gene expression changes in their skeletal muscle. The APP/PS1 mice were subjected to a treadmill exercise for 12 weeks, followed by the Morris water maze and the open field test. After behavioral experiments, the changes in morphology, area, collagen fiber deposition, and ultrastructure of the skeletal muscle were determined; the balance of skeletal muscle protein synthesis and decomposition was analyzed; and changes in gene expression were investigated using RNA-Seq. We found that this exercise strategy can promote the learning and memory abilities of AD mice, reduce their anxiety-like behavior, improve their exercise ability, alleviate skeletal muscle atrophy, and optimize the microstructure. It can also enhance skeletal muscle protein synthesis and decomposition and improve several signaling pathways, such as the JAK–STAT, Wnt, and NOD-like receptors while decreasing calcium, cAMP, cGMP–PKG, and other signaling pathways. Six KEGG enrichment signaling pathways were downregulated and five signaling pathways were upregulated in the AD mice compared with wild-type mice, and these pathways were precisely reversed after the treadmill exercise. The expression of transcription factors such as Fosb and Egr1 in the skeletal muscle of AD mice decreased, followed by a decrease in the regulated target genes Socs1, Srrm4, and Il1b, a trend that was reversed following the exercise intervention. After exercise, AD mice exhibited a similar gene expression to that of wild-type mice, indicating enhanced exercise ability. The potential regulatory pathways and related genes identified in this study provide valuable insights for the clinical management and treatment of AD.
Metabolic Engineering and Process Intensification for Muconic Acid Production Using Saccharomyces cerevisiae
Authors: Tönjes Sinah , Uitterhaegen Evelien , Palmans Ilse , Ibach Birthe , De Winter Karel , Van Dijck Patrick , Soetaert Wim , Vandecruys Paul
Published: September 2024

The efficient production of biobased organic acids is crucial to move to a more sustainable and eco-friendly economy, where muconic acid is gaining interest as a versatile platform chemical to produce industrial building blocks, including adipic acid and terephthalic acid. In this study, a Saccharomyces cerevisiae platform strain able to convert glucose and xylose into cis,cis-muconic acid was further engineered to eliminate C2 dependency, improve muconic acid tolerance, enhance production and growth performance, and substantially reduce the side production of the intermediate protocatechuic acid. This was achieved by reintroducing the PDC5 gene and overexpression of QDR3 genes. The improved strain was integrated in low-pH fed-batch fermentations at bioreactor scale with integrated in situ product recovery. By adding a biocompatible organic phase consisting of CYTOP 503 and canola oil to the process, a continuous extraction of muconic acid was achieved, resulting in significant alleviation of product inhibition. Through this, the muconic acid titer and peak productivity were improved by 300% and 185%, respectively, reaching 9.3 g/L and 0.100 g/L/h in the in situ product recovery process as compared to 3.1 g/L and 0.054 g/L/h in the control process without ISPR.
Gut Microbiota Disruption in Hematologic Cancer Therapy: Molecular Insights and Implications for Treatment Efficacy
Authors: Guevara-Ramírez Patricia , Cadena-Ullauri Santiago , Paz-Cruz Elius , Ruiz-Pozo Viviana A. , Tamayo-Trujillo Rafael , Cabrera-Andrade Alejandro , Zambrano Ana Karina
Published: September 2024

Hematologic malignancies (HMs), including leukemia, lymphoma, and multiple myeloma, involve the uncontrolled proliferation of abnormal blood cells, posing significant clinical challenges due to their heterogeneity and varied treatment responses. Despite recent advancements in therapies that have improved survival rates, particularly in chronic lymphocytic leukemia and acute lymphoblastic leukemia, treatments like chemotherapy and stem cell transplantation often disrupt gut microbiota, which can negatively impact treatment outcomes and increase infection risks. This review explores the complex, bidirectional interactions between gut microbiota and cancer treatments in patients with HMs. Gut microbiota can influence drug metabolism through mechanisms such as the production of enzymes like bacterial β-glucuronidases, which can alter drug efficacy and toxicity. Moreover, microbial metabolites like short-chain fatty acids can modulate the host immune response, enhancing treatment effectiveness. However, therapy often reduces the diversity of beneficial bacteria, such as Bifidobacterium and Faecalibacterium, while increasing pathogenic bacteria like Enterococcus and Escherichia coli. These findings highlight the critical need to preserve microbiota diversity during treatment. Future research should focus on personalized microbiome-based therapies, including probiotics, prebiotics, and fecal microbiota transplantation, to improve outcomes and quality of life for patients with hematologic malignancies.
Influence of the Bone Marrow Microenvironment on Hematopoietic Stem Cell Behavior Post-Allogeneic Transplantation: Development of Clonal Hematopoiesis and Telomere Dynamics
Authors: Kim Myungshin , Kang Dain , Kim Hoon Seok , Lee Jong-Mi , Park Silvia , Kwag Daehun , Lee Chaeyeon , Hong Yuna , Na Duyeon , Koh Youngil , Sun Choong Hyun , An Hongyul , Kim Yoo-Jin , Kim Yonggoo
Published: September 2024

Allogeneic hematopoietic stem cell transplantation (allo-HSCT) is a potential cure for myelodysplastic neoplasms (MDSs) and other hematologic malignancies. This study investigates post-transplantation genetic evolution and telomere dynamics in hematopoietic cells, with a focus on clonal hematopoiesis (CH). We conducted a longitudinal analysis of 21 MDS patients who underwent allo-HSCT between September 2009 and February 2015. Genetic profiles of hematopoietic cells from both recipients and donors were compared at equivalent pre- and post-transplantation time points. Targeted sequencing identified CH-associated mutations, and real-time quantitative PCR measured telomere length. Furthermore, we compared CH incidence between recipients and age-matched controls from the GENIE cohort from routine health checkups. Post-allo-HSCT, 38% of recipients developed somatic mutations not detected before transplantation, indicating de novo CH originating from donor cells. Compared to age-matched healthy controls, recipients showed a significantly higher incidence of CH, suggesting increased susceptibility to genetic changes post-transplant. Telomere length analysis also revealed accelerated shortening in transplanted cells, highlighting the heightened stress and proliferation demands in the new microenvironment. Our findings reveal a notable incidence of donor-derived CH in allo-HSCT recipients, alongside significant telomere attrition. This suggests the potential influence of the marrow microenvironment on genetic and molecular changes in hematopoietic cells.
LC–HRMS Lipidomic Fingerprints in Serbian Cohort of Schizophrenia Patients
Authors: Marković Suzana , Jadranin Milka , Miladinović Zoran , Gavrilović Aleksandra , Avramović Nataša , Takić Marija , Tasic Ljubica , Tešević Vele , Mandić Boris
Published: September 2024

Schizophrenia (SCH) is a major mental illness that causes impaired cognitive function and long-term disability, so the requirements for reliable biomarkers for early diagnosis and therapy of SCH are essential. The objective of this work was an untargeted lipidomic study of serum samples from a Serbian cohort including 30 schizophrenia (SCH) patients and 31 non-psychiatric control (C) individuals by applying liquid chromatography (LC) coupled with high-resolution mass spectrometry (HRMS) and chemometric analyses. Principal component analysis (PCA) of all samples indicated no clear separation between SCH and C groups but indicated clear gender separation in the C group. Multivariate statistical analyses (PCA and orthogonal partial least squares discriminant analysis (OPLS-DA)) of gender-differentiated SCH and C groups established forty-nine differential lipids in the differentiation of male SCH (SCH-M) patients and male controls (C-M), while sixty putative biomarkers were identified in the differentiation of female SCH patients (SCH-F) and female controls (C-F). Lipidomic study of gender-differentiated groups, between SCH-M and C-M and between SCH-F and C-F groups, confirmed that lipids metabolism was altered and the content of the majority of the most affected lipid classes, glycerophospholipids (GP), sphingolipids (SP), glycerolipids (GL) and fatty acids (FA), was decreased compared to controls. From differential lipid metabolites with higher content in both SCH-M and SCH-F patients groups compared to their non-psychiatric controls, there were four common lipid molecules: ceramides Cer 34:2, and Cer 34:1, lysophosphatidylcholine LPC 16:0 and triacylglycerol TG 48:2. Significant alteration of lipids metabolism confirmed the importance of metabolic pathways in the pathogenesis of schizophrenia.