bluuebunny/crossref_metadata_2025 huggingface dataset:
Assessing the Professionalism of Teacher Educators in Relation to Sustainability: Developing the Teacher Education and Sustainability Scale (TESS)
Authors: Dittrich Ann-Kathrin , Eloff Irma , Boon Wietske , Weinberg Lucas , Rabani Nia Maryam , Mathabathe Kgadi Clarrie , Agostini Evi
Published: September 2024

This study focuses on the design and implementation of the Teacher Education and Sustainability Scale (TESS), an instrument to assess the professionalism of teacher educators in relation to sustainability in the context of Agenda 2030 and its 17 Sustainable Development Goals (SDGs). Despite the importance of the SDGs, there is very little research on the identity of teacher educators in relation to sustainability. The Teach4Reach 2.0 project, an international collaboration between the University of Pretoria, the University of Vienna and the University of Innsbruck, aims to fill the research gap in the quantitative measurement of this key area by examining the professionalism of teacher educators in relation to sustainability in order to strengthen their identity. The TESS questionnaire was developed in a structured four-stage process that initially included AI-generated items and was followed by expert refinement and a testing phase with participants from Austria and South Africa. The findings discuss the process of developing the TESS questionnaire and include a critical reflection on AI and on the need for targeted professional development for teacher educators in the field of sustainable development.
An Algorithm for Detecting and Restoring Tampered Images Using Chaotic Watermark Embedding
Authors: Xu Zijie , Wang Erfu
Published: September 2024

In recent years, the advancement of digital image processing technology and the proliferation of image editing software have reduced the technical barriers to digital image processing, enabling individuals without professional training to modify and edit images at their discretion. Consequently, the integrity and authenticity of the original image content assume greater significance. The current techniques for detecting tampering in watermark embedding are inadequate in terms of security, efficiency, and image restoration quality. In light of the aforementioned considerations, this paper puts forth an algorithm for the detection and restoration of tampered images, which employs a chaotic watermark embedding technique. The algorithm employs a chaotic system to establish a mapping relationship between image sub-blocks, thereby ensuring the randomness of the watermark information with respect to the positioning of the original image block and enhancing the security of the algorithm. Furthermore, the detection algorithm utilizes layered tampering detection to enhance the overall accuracy of the detection process and facilitate the extraction of the fundamental information required for image restoration. The restoration algorithm partially designs a weight assignment function to distinguish between the original image block and the main restored image block, thereby enhancing restoration efficiency and quality. The experimental results demonstrate that the proposed algorithm exhibits superior tamper detection accuracy compared to traditional algorithms, and the quality of the restored images is also enhanced under various simulated tamper attacks.
Dual Convolutional Malware Network (DCMN): An Image-Based Malware Classification Using Dual Convolutional Neural Networks
Authors: Al-Masri Bassam , Bakir Nader , El-Zaart Ali , Samrouth Khouloud
Published: September 2024

Malware attacks have a cascading effect, causing financial harm, compromising privacy, operations and interrupting. By preventing these attacks, individuals and organizations can safeguard the valuable assets of their operations, and gain more trust. In this paper, we propose a dual convolutional neural network (DCNN) based architecture for malware classification. It consists first of converting malware binary files into 2D grayscale images and then training a customized dual CNN for malware multi-classification. This paper proposes an efficient approach for malware classification using dual CNNs. The model leverages the complementary strengths of a custom structure extraction branch and a pre-trained ResNet-50 model for malware image classification. By combining features extracted from both branches, the model achieved superior performance compared to a single-branch approach.
Hardware-in-the-Loop Simulation of Flywheel Energy Storage Systems for Power Control in Wind Farms
Authors: Yang Li , Zhao Qiaoni
Published: September 2024

Flywheel energy storage systems (FESSs) are widely used for power regulation in wind farms as they can balance the wind farms’ output power and improve the wind power grid connection rate. Due to the complex environment of wind farms, it is costly and time-consuming to repeatedly debug the system on-site. To save research costs and shorten research cycles, a hardware-in-the-loop (HIL) testing system was built to provide a convenient testing environment for the research of FESSs on wind farms. The focus of this study is the construction of mathematical models in the HIL testing system. Firstly, a mathematical model of the FESS main circuit is established using a hierarchical method. Secondly, the principle of the permanent magnet synchronous motor (PMSM) is analyzed, and a nonlinear dq mathematical model of the PMSM is established by referring to the relationship among d-axis inductance, q-axis inductance, and permanent magnet flux change with respect to the motor’s current. Then, the power grid and wind farm test models are established. Finally, the established mathematical models are applied to the HIL testing system. The experimental results indicated that the HIL testing system can provide a convenient testing environment for the optimization of FESS control algorithms.
Digital Twin for Modern Distribution Networks by Improved State Estimation with Consideration of Bad Date Identification
Authors: Zhi Huiqiang , Mao Rui , Hao Longfei , Chang Xiao , Guo Xiangyu , Ji Liang
Published: September 2024

With the rapid development of modern power systems, the structure and operation of distribution networks are becoming increasingly complex, demanding higher levels of intelligence and digitization. Digital twin, as a virtual cutting-edge technique, can effectively reflect the operational status of distribution networks, offering new possibilities for real-time monitoring, optimization and other functions for distribution networks. Building efficient and accurate models is the foundation of enabling a digital twin of distribution networks. This paper proposes a digital twin operating system for distribution networks with renewable energy based on robust state estimation and deep learning-based renewable energy prediction. Furthermore, the identification and correction of possible bad or missing data based on deep learning are also included to purify the input data for the digital twin system. A digital twin test platform is also proposed in the paper. A case study and evaluations based on a real-time digital simulator are carried out to verify the accuracy and real-time performance of the established digital twin system. In general, the proposed method can provide the basis and foundation for distribution network management and operation, as well as intelligent power system operation.
Challenges in Information Systems Curricula: Effectiveness of Systems Application Products in Data Processing Learning in Higher Education through a Technological, Organizational and Environmental Framework
Authors: Banța Viorel-Costin , Bunea Ștefan , Țuțui Daniela , Crețu Raluca Florentina
Published: September 2024

Higher education institutions are increasingly concerned with providing students with sustainable education by developing the necessary competencies for various roles in the business environment. To be more effective, courses must develop technological, organizational and environmental (TOE) competencies in an integrated manner. SAP is a tool that yields this possibility through the diversity of IT solutions by ensuring a significant increase in employability rates. Learning SAP is a competitive advantage because it helps with all aspects of digital transformation within the concept of Industry 4.0. Our research aims to investigate to what extent students perceive that they have acquired the knowledge and competencies specific to the three dimensions of the TOE framework within the SAP course. We have added a fourth dimension to the TOE framework: the learning context (L) considering the impact of the educational environment on perceived learning outcomes. Data collection was based on a questionnaire distributed to students enrolled in the SAP course in the academic year 2023–2024 at Bucharest University of Economic Studies (BUES). The data were processed using correlation and regression analysis. Reconfiguring the content elements of SAP courses based on the TOE framework would ensure greater effectiveness in the learning process.
Assessing the Resilience of Enteric Bacteria in Manure in Response to Changes in Relative Humidity and UV-B Light
Authors: Leon Ingrid M. , Auvermann Brent W. , Bush Kevin Jack , Casey Kenneth , Pinchak William E. , Vinasco Javier , Lawhon Sara D. , Smith Jason K. , Scott Harvey Morgan , Norman Keri N.
Published: September 2024

Dehydrated manure from agricultural animal feedlots can become aerosolized and may potentially harbor viable antimicrobial-resistant bacteria. Little is known about the dynamics and risk of bacteria in bioaerosols originating from the feedyard environment. Nutrient deficiency, desiccation, UV exposure, temperature, and pH changes can affect bacterial viability. In this study, we investigated the impact of changes in relative humidity (RH) and UV-B exposure on enteric bacterial survival in vitro to simulate environmental conditions in cattle feedyards. Cattle manure samples were placed in two separate chambers with 73% RH and 31% RH, respectively. For the UV-B experiment, samples were placed in a chamber exposed to UV-B (treated) or in a chamber exposed to LED light (control). Samples from both experiments were spiral plated in triplicate onto selective agar media to quantify total aerobic bacteria, E. coli (total and antimicrobial-resistant (AMR)), and Enterococcus spp. (total and AMR). Results showed that enteric bacteria from cattle manure can withstand at least two stress conditions, including low RH levels and UV-B exposure. Moreover, the data revealed that antimicrobial-resistant bacteria can persist in manure under the harsh conditions that may be encountered in a feedyard environment. These findings underscore the need for mitigation strategies in feedlots to minimize the overall risk of bioaerosol formation.
Influence of Elevation and Stand Age on the Abundance of the Beech Bark Beetle (Taphrorychus bicolor Her.) and Its Potential Threat to Beech Stands
Authors: Špoula Jakub , Véle Adam , Neudertová Hellebrandová Kateřina
Published: September 2024

In 2023, branches of European beech (Fagus sylvatica L.) were placed on 24 different study sites within beech stands in the Czech Republic to study the distribution of the beech bark beetle (Taphrorychus bicolor Her.). After infestation, the branches were placed in emergence traps to capture adults of the offspring generation. In total, 2167 adults of T. bicolor were captured across the 24 study sites. The average capture (mean ± SE) was 90.3 ± 24.4 adults per site. Statistical analyses, including GLMM and GLM, were employed to assess the influence of elevation and stand age on the abundance of T. bicolor. The results indicate that the distribution of T. bicolor is mainly influenced by the elevation and age of the beech stand. The number of captured adults decreased with elevation and increased with stand age. Climatic region, volumes of beech, and volumes of felled beech wood from principal felling and thinning felling had no significant effect on the number of T. bicolor adults captured. We found that, overall, 73.37% of Czech beech stands (at elevations 300–650 m a.s.l.) are possibly threatened (either highly or slightly) by the large occurrence of T. bicolor, because they are present at elevations where T. bicolor is widespread. No outbreaks of T. bicolor have yet been reported in the Czech Republic, but with the increasing amount of beech planted, combined with global climate change, the possibility of outbreaks in the future cannot be excluded.
Strength Grading of Full-Scale Chinese Fast-Growing Poplar Wood for Structural Building Applications
Authors: Qian Jin , Yue Kong , Li Xiang , Pu Yan , Chen Kaiyi , Wu Peng , Zhang Zhongfeng
Published: September 2024

China boasts the world’s largest plantation forest of fast-growing poplar trees. However, the wood from these plantations typically exhibits high moisture content, leading to issues such as cracking and warping upon drying. The primary objective of this study was to evaluate the statistical suitability of Weibull, normal, and log-normal distributions for modeling the modulus of elasticity (MOE) of timber and to classify the strength of fast-growing poplar wood based on its strength characteristics to facilitate standardized utilization. Visual grading was employed to remove wood having apparent defects, thus mitigating the influence of wood defects and drying processes on strength. Subsequently, machine grading was conducted using static bending tests to assess the applicability of normal, log-normal, and Weibull distributions to the modulus of elasticity (MOE) distribution. Additionally, the study utilized a correlation matrix to explore the impact of density and moisture content on MOE. The findings suggest that both normal and Weibull distributions are suitable for characterizing the MOE of Chinese fast-growing poplar wood, while the log-normal distribution is not. The mean characteristic values of the MOE and density were 12.21 GPa and 521 kg/m3, respectively. The sampled poplar wood was categorized as C30 grade. Both density and moisture content were found to exert significant influences on the MOE (p < 0.01). However, density alone is not a reliable predictor for estimating MOE (R2 = 0.511).
The Beneficial Elements in Forest Environment Based on Human Health and Well-Being Perspective
Authors: Gao Deqiang , Shen Jiapeng , Gao Yunchang , Zhang Zhiyong
Published: September 2024

Illness is a significant global societal issue in the 21st century. Forest, as an important part of terrestrial ecosystem, holds substantial health and well-being benefits. People can gain health benefits from interacting with forests, even for short periods. Unfortunately, there is a lack of systematic concern regarding the beneficial elements that forest provides to humans. In this study, a systematic review and meta-analysis were conducted following established guidelines, comprehensively evaluating the beneficial elements of the forest environment. The results indicated that the beneficial forest elements relevant to human health include beneficial substances (clean air, high-quality freshwater, CO2/O2 balance, negative air ions, and phytoncides) and beneficial factors (moderate thermal environment and biodiversity). These beneficial forest elements are products of plant’s physiological processes. While their production pathways are relatively well understood, the mechanisms by which these elements impact health are unclear. This review provided the foundational data and theoretical insights for future research on the health benefits of forest elements.
Potential of Crude Extract of Streptomyces sp. nov., Strain TRM76147 for Control of A. gaisen
Authors: Chen Yi-Huang , Zhang Jia-Xin , Yang Guo , Liu Yang , Ran Song , Wang Jian-Ming , Liu Qin , Luo Xiao-Xia
Published: September 2024

Pear black spot, caused by A. gaisen during fruit growth, is a disease that significantly reduces pear yield. Biological control using antagonistic microorganisms is regarded as a viable alternative to chemical agents. The discovery of TRM76147, a novel species of Streptomyces isolated from the Taklamakan Desert, has demonstrated promising potential in addressing this issue. This study was conducted to determine the potential of crude extract of Streptomyces sp. nov., strain TRM76147, for control of A. gaisen. TRM76147 is closely related to Streptomyces griseoviridis NBRC 12874T, exhibiting an average nucleotide identity (ANI) value of 82.13%. Combined with the polyphasic taxonomic identification, this suggests that TRM76147 is a potentially new species. Through analyses using BigSCAPE and antiSMASH, it was determined that the TRM76147 genome contains 19 gene clusters. The ethyl acetate extract of this strain demonstrates antifungal activity, with the active substance remaining stable at temperatures up to 70 °C, achieving an activity level of 16.23 ± 0.22 mm. Furthermore, the crude extract maintains its antifungal efficacy across a pH range of 2 to 12. Notably, the antifungal diameter was recorded at 16.53 ± 0.12 mm following 80 min of UV irradiation. Under different treatment conditions, TRM76147 fermentation crude extract caused A. gaisen spore crumpling and spore number reduction. In addition, this study also found that the TRM76147 fermentation broth could control the production of pear black spot disease, which initially revealed the inhibition mechanism. The abundant actinomycete resources in this study have good application and development value in the discovery of new species and the study of bioactive substances and biological control.
A Complete EDA and DL Pipeline for Softwarized 5G Network Intrusion Detection
Authors: Moubayed Abdallah
Published: September 2024

The rise of 5G networks is driven by increasing deployments of IoT devices and expanding mobile and fixed broadband subscriptions. Concurrently, the deployment of 5G networks has led to a surge in network-related attacks, due to expanded attack surfaces. Machine learning (ML), particularly deep learning (DL), has emerged as a promising tool for addressing these security challenges in 5G networks. To that end, this work proposed an exploratory data analysis (EDA) and DL-based framework designed for 5G network intrusion detection. The approach aimed to better understand dataset characteristics, implement a DL-based detection pipeline, and evaluate its performance against existing methodologies. Experimental results using the 5G-NIDD dataset showed that the proposed DL-based models had extremely high intrusion detection and attack identification capabilities (above 99.5% and outperforming other models from the literature), while having a reasonable prediction time. This highlights their effectiveness and efficiency for such tasks in softwarized 5G environments.
Prediction of Ceiling Temperature Rise in High-Voltage Cable Trenches with Identification of Ignition Points
Authors: Zhang Zhaochen , Zou Liang , Yang Hongmin , Han Zhiyun
Published: September 2024

Early detection of cable trench fires by locating the fire source in a timely manner can reduce the risk of fire. However, existing fire warning methods have low accuracy, long calculation times and difficulty coping with sudden fire situations. We established experimental platforms for cable trenches with different structures and combined these with simulation analysis to investigate the relationship between the ignition point position and the temperature distribution at the ceiling. An exponential function for predicting the ignition point position and the maximum temperature rise of tunnels is proposed based on the extreme values of ceiling temperature. The results indicate that the vertical temperature of the ceiling exhibits an exponential function variation pattern. The maximum deviation for identifying the ignition point is 0.098 m, with an average deviation of 0.044 m and an average accuracy of 98.77%. The maximum temperature prediction error for the ceiling is 14 °C, with an average deviation of 12.33 °C and an average accuracy of 98.30%. Compared to traditional fire prediction methods, the method proposed here has higher accuracy and provides a theoretical basis for early prevention and control of cable trench fires.
Effect of Integrated Extraction Techniques on the Technofunctional and Bioactive Properties of Brosimum alicastrum Swartz Proteins
Authors: Suárez-Hernández María Fernanda , Posada Ramirez Sara Gabriela , Castillo Cruz Darling del Carmen , Higuera Ciapara Inocencio , Pacheco López Neith Aracely , Herrera Pool Iván Emanuel , Ruiz-Ruiz Jorge Carlos
Published: September 2024

This study addresses the need for effective protein extraction and characterization to unlock the potential of underutilized plant resources like Brosimum alicastrum Swartz nuts, aiming to enhance their value as functional ingredients in food applications. Extraction methods, including pH modulation, ultrasound-assisted extraction, and enzymatic hydrolysis, are employed to enhance technofunctional and bioactive properties. The protein extracts are evaluated for solubility, emulsifying capacity, foaming properties, and water/oil-holding capacities to assess their technofunctional potential. Additionally, the bioactive properties, such as antioxidant and anti-inflammatory activities, are analyzed to explore potential health benefits. The results demonstrate that integrated extraction techniques significantly improve the yield and quality of Brosimum alicastrum Swartz nut proteins. Enzymatic hydrolysis, in particular, produces hydrolysates with superior bioactive properties. These findings highlight the potential of Brosimum alicastrum Swartz proteins as valuable ingredients for the food and pharmaceutical industries, promoting the utilization of underexploited plant resources for sustainable and health-promoting applications.
Arrest and Attack: Microtubule-Targeting Agents and Oncolytic Viruses Employ Complementary Mechanisms to Enhance Anti-Tumor Therapy Efficacy
Authors: De Sucheta , Ehrlich Marcelo
Published: September 2024

Oncolytic viruses (OVs) are promising cancer immunotherapy agents that stimulate anti-tumor immunity through the preferential infection and killing of tumor cells. OVs are currently under limited clinical usage, due in part to their restricted efficacy as monotherapies. Current efforts for enhancement of the therapeutic potency of OVs involve their combination with other therapy modalities, aiming at the concomitant exploitation of complementary tumor weaknesses. In this context, microtubule-targeting agents (MTAs) pose as an enticing option, as they perturb microtubule dynamics and function, induce cell-cycle arrest, and cause mitotic cell death. MTAs induce therapeutic benefit through cancer-cell-autonomous and non-cell-autonomous mechanisms and are a main component of the standard of care for different malignancies. However, off-target effects and acquired resistance involving distinct cellular and molecular mechanisms may limit the overall efficacy of MTA-based therapy. When combined, OVs and MTAs may enhance therapeutic efficacy through increases in OV infection and immunogenic cell death and a decreased probability of acquired resistance. In this review, we introduce OVs and MTAs, describe molecular features of their activity in cancer cells, and discuss studies and clinical trials in which the combination has been tested.
Developing a Machine-Learning ‘Smart’ PCR Thermocycler, Part 1: Construction of a Theoretical Framework
Authors: McDonald Caitlin , Taylor Duncan , Masawi Gershom Mwachari , Khan Ayesha Khalid Ahmed , Leibbrandt Richard , Linacre Adrian , Brinkworth Russell S. A.
Published: September 2024

The use of PCR is widespread in biological fields. Some fields, such as forensic biology, push PCR to its limits as DNA profiling may be required in short timeframes, may be produced from minute amounts of starting material, and may be required to perform in the presence of inhibitory compounds. Due to the extreme high-throughput of samples using PCR in forensic science, any small improvement in the ability of PCR to address these challenges can have dramatic effects for the community. At least part of the improvement in PCR performance could potentially come by altering PCR cycling conditions. These alterations could be general, in that they are applied to all samples, or they could be tailored to individual samples for maximum targeted effect. Further to this, there may be the ability to respond in real time to the conditions of PCR for a sample and make cycling parameters change on the fly. Such a goal would require both a means to track the conditions of the PCR in real time, and the knowledge of how cycling parameters should be altered, given the current conditions. In Part 1 of our work, we carry out the theoretical groundwork for the ambitious goal of creating a smart PCR system that can respond appropriately to features within individual samples in real time. We approach this task using an open qPCR instrument to provide real-time feedback and machine learning to identify what a successful PCR ‘looks like’ at different stages of the process. We describe the fundamental steps to set up a real-time feedback system, devise a method of controlling PCR cycling conditions from cycle to cycle, and to develop a system of defining PCR goals, scoring the performance of the system towards achieving those goals. We then present three proof-of-concept studies that prove the feasibility of this overall method. In a later Part 2 of our work, we demonstrate the performance of the theory outlined in this paper on a large-scale PCR cycling condition alteration experiment. The aim is to utilise machine learning so that throughout the process of PCR automatic adjustments can be made to best alter cycling conditions towards a user-defined goal. The realisation of smart PCR systems will have large-scale ramifications for biological fields that utilise PCR.
Effects of Grazing and Shrub Management on Species Composition and Soil Properties in Patagonian Grasslands
Authors: Vogel Braian , Molina Lucia , Rostagno César M. , La Manna Ludmila
Published: September 2024

Historical sheep farming in the Patagonian drylands has led to reduced grass cover, soil erosion, and shrub encroachment, compromising ecosystem function. Effective restoration requires managing shrub cover, bare soil, and patch connectivity through various strategies. This study evaluates rehabilitation interventions in a grass-steppe ecosystem, comparing grazed and ungrazed areas. Over three years, we tested the following: (a) mechanical shrub cutting with biomass redistribution, and (b) enhancing patch connectivity with Pinus spp. branch piles, alongside controls, in eighteen 5 m × 5 m plots invaded by Mulinum spinosum. Half of the plots were fenced to exclude grazing, resulting in six treatment combinations. We monitored soil properties, vegetation cover, and species composition. The treatments explained twice as much of the variation in community composition as the annual climatic variations (0.26 vs. 0.13). Livestock exclusion increased perennial grass cover more than the grazed plots did (2.14 vs. 1.42 times the initial measure). All treatments reduced the amount of bare soil except the grazed controls. Shrub cutting, especially with grazing, increased the lasting litter coverage by 5–10% and decreased the bare soil equivalently. Organic matter increased except in the non-intervened interpatches (0.95 times). The enclosures with cut shrubs trapped erodible particles, showing a 5% increase. Our study highlights that grazing destabilizes communities, while enclosures stabilize them, with interventions improving soil fertility and mitigating erosion.
Long-Term Functional Outcomes and Quality of Life Following Carpal Tunnel Release Surgery
Authors: Krieger Yuval , Ofri Mai , Sagi Gali , Moshe-Noach Hila , Raiden Pnina , Shoham Yaron , Sagi Amiram , Reiner Benaim Anat , Silberstein Eldad
Published: September 2024

This study investigates the long-term efficacy of carpal tunnel release surgery (CTR) on patient outcomes. We aimed to assess symptom severity, functional limitations, and quality of life in a large cohort (n = 186) at least five years post-surgery via a retrospective cross-sectional design that evaluated participants using the validated Boston Carpal Tunnel Questionnaire (BCTQ) over a phone interview. The BCTQ measures symptom severity, functional limitations, and quality of life specific to carpal tunnel syndrome. Seventy-three percent (73.1%) of patients reported complete resolution of symptoms and functional limitations (BCTQ = 1) with an average follow-up of 11 years. No statistically significant decline in BCTQ scores was observed over time. Univariate analysis revealed a significant association between poorer outcomes and older age at surgery and current unemployment. Carpal tunnel release surgery demonstrates long-term effectiveness in relieving symptoms and improving function in most patients. These findings contribute to the understanding of CTR’s impact on patient well-being beyond short-term outcomes.
Hypoxia in Human Obesity: New Insights from Inflammation towards Insulin Resistance—A Narrative Review
Authors: Mirabelli Maria , Misiti Roberta , Sicilia Luciana , Brunetti Francesco S. , Chiefari Eusebio , Brunetti Antonio , Foti Daniela P.
Published: September 2024

Insulin resistance (IR), marked by reduced cellular responsiveness to insulin, and obesity, defined by the excessive accumulation of adipose tissue, are two intertwined conditions that significantly contribute to the global burden of cardiometabolic diseases. Adipose tissue, beyond merely storing triglycerides, acts as an active producer of biomolecules. In obesity, as adipose tissue undergoes hypertrophy, it becomes dysfunctional, altering the release of adipocyte-derived factors, known as adipokines. This dysfunction promotes low-grade chronic inflammation, exacerbates IR, and creates a hyperglycemic, proatherogenic, and prothrombotic environment. However, the fundamental cause of these phenomena remains unclear. This narrative review points to hypoxia as a critical trigger for the molecular changes associated with fat accumulation, particularly within visceral adipose tissue (VAT). The activation of hypoxia-inducible factor-1 (HIF-1), a transcription factor that regulates homeostatic responses to low oxygen levels, initiates a series of molecular events in VAT, leading to the aberrant release of adipokines, many of which are still unexplored, and potentially affecting peripheral insulin sensitivity. Recent discoveries have highlighted the role of hypoxia and miRNA-128 in regulating the insulin receptor in visceral adipocytes, contributing to their dysfunctional behavior, including impaired glucose uptake. Understanding the complex interplay between adipose tissue hypoxia, dysfunction, inflammation, and IR in obesity is essential for developing innovative, targeted therapeutic strategies.
Hepatic Proteomic Changes Associated with Liver Injury Caused by Alcohol Consumption in Fpr2−/− Mice
Authors: Hardesty Josiah E. , Warner Jeffrey B. , Wilkey Daniel W. , Phinney Brett S. , Salemi Michelle R. , Merchant Michael L. , McClain Craig J. , Warner Dennis R. , Kirpich Irina A.
Published: September 2024

Alcohol-associated liver disease (ALD) is a prevalent medical problem with limited effective treatment strategies. Although many biological processes contributing to ALD have been elucidated, a complete understanding of the underlying mechanisms is still lacking. The current study employed a proteomic approach to identify hepatic changes resulting from ethanol (EtOH) consumption and the genetic ablation of the formyl peptide receptor 2 (FPR2), a G-protein coupled receptor known to regulate multiple signaling pathways and biological processes, in a mouse model of ALD. Since previous research from our team demonstrated a notable reduction in hepatic FPR2 protein levels in patients with alcohol-associated hepatitis (AH), the proteomic changes in the livers of Fpr2−/− EtOH mice were compared to those observed in patients with AH in order to identify common hepatic proteomic alterations. Several pathways linked to exacerbated ALD in Fpr2−/− EtOH mice, as well as hepatic protein changes resembling those found in patients suffering from AH, were identified. These alterations included decreased levels of coagulation factors F2 and F9, as well as reduced hepatic levels of glutamate-cysteine ligase catalytic subunit (GCLC) and total glutathione in Fpr2−/− EtOH compared to WT EtOH mice. In conclusion, the data suggest that FPR2 may play a regulatory role in hepatic blood coagulation and the antioxidant system, both in a pre-clinical model of ALD and in human AH, however further experiments are required to validate these findings.
PRC1 Protein Subcomplexes Architecture: Focus on the Interplay between Distinct PCGF Subunits in Protein Interaction Networks
Authors: Munawar Nayla , Wynne Kieran , Oliviero Giorgio
Published: September 2024

The six PCGF proteins (PCGF1-6) define the biochemical identity of Polycomb repressor complex 1 (PRC1) subcomplexes. While structural and functional studies of PRC1 subcomplexes have revealed their specialized roles in distinct aspects of epigenetic regulation, our understanding of the variation in the protein interaction networks of distinct PCGF subunits in different PRC1 complexes is incomplete. We carried out an affinity purification mass spectrometry (AP-MS) screening of three PCGF subunits, PCGF1 (NSPC1), PCGF2 (MEL18), and PCGF4 (BMI1), to define their interactome and potential cellular function in pluripotent human embryonal carcinoma cell “NT2”. The bioinformatic analysis revealed that these interacting proteins cover a range of functional pathways, often involved in cell biology and chromatin regulation. We also found evidence of mutual regulation (at mRNA and protein level) between three distinct PCGF subunits. Furthermore, we confirmed that the disruption of these subunits results in reduced cell proliferation ability. We reveal an interplay between the compositional diversity of the distinct PCGF containing PRC1 complex and the potential role of PCGF proteins within the wider cellular network.
Transcriptome Analysis and Identification of Genes Associated with Cotton Seed Size
Authors: Jia Bing , Feng Pan , Song Jikun , Zhou Caoyi , Wang Yajie , Zhang Bingbing , Wu Man , Zhang Jinfa , Chen Quanjia , Yu Jiwen
Published: September 2024

Cotton seeds, as the main by-product of cotton, are not only an important raw material for edible oil and feed but also a source of biofuel. The quality of cotton seeds directly affects cotton planting and is closely related to the yield and fiber quality. However, the molecular mechanism governing cotton seed size remains largely unexplored. This study investigates the regulatory mechanisms of cotton seed size by focusing on two cotton genotypes, N10 and N12, which exhibit notable phenotypic variations across multiple environments. Developing seeds were sampled at various stages (5, 20, 30, and 35 DPA) and subjected to RNA-seq. Temporal pattern clustering and WGCNA on differentially expressed genes identified 413 candidate genes, including these related to sugar metabolism that were significantly enriched in transcriptional regulation. A genetic transformation experiment indicated that the overexpression of the GhUXS5 gene encoding UDP-glucuronate decarboxylase 5 significantly increased seed size, suggesting an important role of GhUXS5 in regulating cotton seed size. This discovery provides crucial insights into the molecular mechanisms controlling cotton seed size, helping to unravel the complex regulatory network and offering new strategies and targets for cotton breeding to enhance the economic value of cotton seeds and overall cotton yield.
Analysis of the Rice Raffinose Synthase (OsRS) Gene Family and Haplotype Diversity
Authors: Zhang Jinguo , Meng Dezhuang , Li Jianfeng , Bao Yaling , Yu Peng , Dou Guohui , Guo Jinmeng , Tang Chenghang , Lv Jiaqi , Wang Xinchen , Wang Xingmeng , Wu Fengcai , Shi Yingyao
Published: September 2024

Based on the genome information of rice (Nipponbare), this study screened and identified six raffinose synthase (RS) genes and analyzed their physical and chemical properties, phylogenetic relationship, conserved domains, promoter cis-acting elements, and the function and genetic diversity of the gene-CDS-haplotype (gcHap). The results showed that these genes play key roles in abiotic stress response, such as OsRS5, whose expression in leaves changed significantly under high salt, drought, ABA, and MeJA treatments. In addition, the OsRS genes showed significant genetic variations in different rice populations. The main gcHaps of most OsRS loci had significant effects on key agronomic traits, and the frequency of these alleles varied significantly among different rice populations and subspecies. These findings provide direction for studying the RS gene family in other crops.
Combining rVAR2 and Anti-EpCAM to Increase the Capture Efficiency of Non-Small-Cell Lung Cancer Cell Lines in the Flow Enrichment Target Capture Halbach (FETCH) Magnetic Separation System
Authors: He Sitian , Liu Peng , Wu Yongjun , Agerbæk Mette Ø. , Salanti Ali , Terstappen Leon W. M. M. , Jonkheijm Pascal , Stevens Michiel
Published: September 2024

Circulating tumor cells (CTCs) are detected in approximately 30% of metastatic non-small-cell lung cancer (NSCLC) cases using the CellSearch system, which relies on EpCAM immunomagnetic enrichment and Cytokeratin detection. This study evaluated the effectiveness of immunomagnetic enrichment targeting oncofetal chondroitin sulfate (ofCS) using recombinant VAR2CSA proteins (rVAR2) to improve the recovery of different NSCLC cell lines spiked into lysed blood samples. Four NSCLC cell lines—NCI-H1563, A549, NCI-H1792, and NCI-H661—were used to assess capture efficiency. The results demonstrated that the combined use of anti-EpCAM antibody and rVAR2 significantly enhanced the capture efficiency to an average of 88.2% compared with 40.6% when using only anti-EpCAM and 56.6% when using only rVAR2. These findings suggest that a dual-marker approach using anti-EpCAM and rVAR2 can provide a more robust and sensitive method for CTC enrichment in NSCLC, potentially leading to better diagnostic and prognostic outcomes.
Molecular Mechanisms Underlying the Generation of Absence Seizures: Identification of Potential Targets for Therapeutic Intervention
Authors: Leitch Beulah
Published: September 2024

Understanding the molecular mechanisms underlying the generation of absence seizures is crucial for developing effective, patient-specific treatments for childhood absence epilepsy (CAE). Currently, one-third of patients remain refractive to the antiseizure medications (ASMs), previously called antiepileptic drugs (AEDs), available to treat CAE. Additionally, these ASMs often produce serious side effects and can even exacerbate symptoms in some patients. Determining the precise cellular and molecular mechanisms directly responsible for causing this type of epilepsy has proven challenging as they appear to be complex and multifactorial in patients with different genetic backgrounds. Aberrant neuronal activity in CAE may be caused by several mechanisms that are not fully understood. Thus, dissecting the causal factors that could be targeted in the development of precision medicines without side effects remains a high priority and the ultimate goal in this field of epilepsy research. The aim of this review is to highlight our current understanding of potential causative mechanisms for absence seizure generation, based on the latest research using cutting-edge technologies. This information will be important for identifying potential targets for future therapeutic intervention.
Itaconic Acid Alleviates Perfluorooctanoic Acid-Induced Oxidative Stress and Intestinal Damage by Regulating the Keap1/Nrf2/Ho-1 Pathway and Reshaping the Gut Microbiota
Authors: Wu Lianchi , Hu Zhaoying , Luo Xinyu , Ge Chaoyue , Lv Yujie , Zhan Shenao , Huang Weichen , Shen Xinyu , Yu Dongyou , Liu Bing
Published: September 2024

Itaconic acid (IA) is recognized for its potential application in treating intestinal diseases owing to the anti-inflammatory and antioxidant properties. Perfluorooctanoic acid (PFOA) can accumulate in animals and result in oxidative and inflammatory damages to multi-tissue and organ, particularly in the intestinal tract. This study aimed to explore whether IA could mitigate intestinal damage induced by PFOA exposure in laying hens and elucidate its potential underlying mechanisms. The results showed that IA improved the antioxidant capacity of laying hens and alleviated the oxidative damage induced by PFOA, as evidenced by the elevated activities of T-SOD, GSH-Px, and CAT, and the decreased MDA content in both the jejunum and serum. Furthermore, IA improved the intestinal morphological and structural integrity, notably attenuating PFOA-induced villus shedding, length reduction, and microvillus thinning. IA also upregulated the mRNA expression of ZO-1, Occludin, Claudin-1, and Mucin-2 in the jejunum, thereby restoring intestinal barrier function. Compared with the PF group, IA supplementation downregulated the gene expression of Keap1 and upregulated the HO-1, NQO1, SOD1, and GPX1 expression in the jejunum. Meanwhile, the PF + IA group exhibited lower expressions of inflammation-related genes (NF-κB, IL-1β, IFN-γ, TNF-α, and IL-6) compared to the PF group. Moreover, IA reversed the PFOA-induced imbalance in gut microbiota by reducing the harmful bacteria such as Escherichia-Shigella, Clostridium innocuum, and Ruminococcus torques, while increasing the abundance of beneficial bacteria like Lactobacillus. Correlation analysis further revealed a significant association between gut microbes, inflammatory factors, and the Keap1/Nrf2/HO-1 pathway expression. In conclusion, dietary IA supplementation could alleviate the oxidative and inflammatory damage caused by PFOA exposure in the intestinal tract by reshaping the intestinal microbiota, modulating the Keap1/Nrf2/HO-1 pathway and reducing oxidative stress and inflammatory response, thereby promoting intestinal homeostasis.
Advanced Prediction of Hepatic Oncogenic Transformation in HBV Patients via RNA-Seq Data Analysis and Deep Learning Techniques
Authors: Li Zhengtai , Huang Lei , Yu Changyuan
Published: September 2024

Liver cancer, recognized as a significant global health issue, is increasingly correlated with Hepatitis B virus (HBV) infection, as evidenced by numerous scientific studies. This study aims to examine the correlation between HBV infection and the development of liver cancer, focusing on using RNA sequencing (RNA-seq) to detect HBV sequences and applying deep learning techniques to estimate the likelihood of oncogenic transformation in individuals with HBV. Our study utilized RNA-seq data and employed Pathseq software and sophisticated deep learning models, including a convolutional neural network (CNN), to analyze the prevalence of HBV sequences in the samples of patients with liver cancer. Our research successfully identified the prevalence of HBV sequences and demonstrated that the CNN model achieved an exceptional Area Under the Curve (AUC) of 0.998 in predicting cancerous transformations. We observed no viral synergism that enhanced the pathogenicity of HBV. A detailed analysis of sequences misclassified by the CNN model revealed that longer sequences were more conducive to accurate recognition. The findings from this study provide critical insights into the management and prognosis of patients infected with HBV, highlighting the potential of advanced analytical techniques in understanding the complex interactions between viral infections and cancer development.
WGCAMNet: Wasserstein Generative Adversarial Network Augmented and Custom Attention Mechanism Based Deep Neural Network for Enhanced Brain Tumor Detection and Classification
Authors: Alam Fatema Binte , Fahim Tahasin Ahmed , Asef Md , Hossain Md Azad , Dewan M. Ali Akber
Published: September 2024

Brain tumor detection and categorization of its subtypes are essential for early diagnosis and improving patient outcomes. This research presents a cutting-edge approach that employs advanced data augmentation and deep learning methodologies for brain tumor classification. For this work, a dataset of 6982 MRI images from the IEEE Data Port was considered, in which a total of 5712 images of four classes (1321 glioma, 1339 meningioma, 1595 no tumor, and 1457 pituitary) were used in the training set and a total of 1270 images of the same four classes were used in the testing set. A Wasserstein Generative Adversarial Network was implemented to generate synthetic images to address class imbalance, resulting in a balanced and consistent dataset. A comparison was conducted between various data augmentation metholodogies demonstrating that Wasserstein Generative Adversarial Network-augmented results perform excellently over traditional augmentation (such as rotation, shift, zoom, etc.) and no augmentation. Additionally, a Gaussian filter and normalization were applied during preprocessing to reduce noise, highlighting its superior accuracy and edge preservation by comparing its performance to Median and Bilateral filters. The classifier model combines parallel feature extraction from modified InceptionV3 and VGG19 followed by custom attention mechanisms for effectively capturing the characteristics of each tumor type. The model was trained for 64 epochs using model checkpoints to save the best-performing model based on validation accuracy and learning rate adjustments. The model achieved a 99.61% accuracy rate on the testing set, with precision, recall, AUC, and loss of 0.9960, 0.9960, 0.0153, and 0.9999, respectively. The proposed architecture’s explainability has been enhanced by t-SNE plots, which show unique tumor clusters, and Grad-CAM representations, which highlight crucial areas in MRI scans. This research showcases an explainable and robust approach for correctly classifying four brain tumor types, combining WGAN-augmented data with advanced deep learning models in feature extraction. The framework effectively manages class imbalance and integrates a custom attention mechanism, outperforming other models, thereby improving diagnostic accuracy and reliability in clinical settings.
Classification of Moral Decision Making in Autonomous Driving: Efficacy of Boosting Procedures
Authors: Singh Amandeep , Murzello Yovela , Pokhrel Sushil , Samuel Siby
Published: September 2024

Autonomous vehicles (AVs) face critical decisions in pedestrian interactions, necessitating ethical considerations such as minimizing harm and prioritizing human life. This study investigates machine learning models to predict human decision making in simulated driving scenarios under varying pedestrian configurations and time constraints. Data were collected from 204 participants across 12 unique simulated driving scenarios, categorized into young (24.7 ± 3.5 years, 38 males, 64 females) and older (71.0 ± 5.7 years, 59 males, 43 females) age groups. Participants’ binary decisions to maintain or change lanes were recorded. Traditional logistic regression models exhibited high precision but consistently low recall, struggling to identify true positive instances requiring intervention. In contrast, the AdaBoost algorithm demonstrated superior accuracy and discriminatory power. Confusion matrix analysis revealed AdaBoost’s ability to achieve high true positive rates (up to 96%) while effectively managing false positives and negatives, even under 1 s time constraints. Learning curve analysis confirmed robust learning without overfitting. AdaBoost consistently outperformed logistic regression, with AUC-ROC values ranging from 0.82 to 0.96. It exhibited strong generalization, with validation accuracy approaching 0.8, underscoring its potential for reliable real-world AV deployment. By consistently identifying critical instances while minimizing errors, AdaBoost can prioritize human safety and align with ethical frameworks essential for responsible AV adoption.