bluuebunny/crossref_metadata_2025 huggingface dataset:
Combining the Strengths of the Explainable Boosting Machine and Metabolomics Approaches for Biomarker Discovery in Acute Myocardial Infarction
Authors: Arslan Ahmet Kadir , Yagin Fatma Hilal , Algarni Abdulmohsen , AL-Hashem Fahaid , Ardigò Luca Paolo
Published: June 2024

Acute Myocardial Infarction (AMI), a common disease that can have serious consequences, occurs when myocardial blood flow stops due to occlusion of the coronary artery. Early and accurate prediction of AMI is critical for rapid prognosis and improved patient outcomes. Metabolomics, the study of small molecules within biological systems, is an effective tool used to discover biomarkers associated with many diseases. This study intended to construct a predictive model for AMI utilizing metabolomics data and an explainable machine learning approach called Explainable Boosting Machines (EBM). The EBM model was trained on a dataset of 102 prognostic metabolites gathered from 99 individuals, including 34 healthy controls and 65 AMI patients. After a comprehensive data preprocessing, 21 metabolites were determined as the candidate predictors to predict AMI. The EBM model displayed satisfactory performance in predicting AMI, with various classification performance metrics. The model’s predictions were based on the combined effects of individual metabolites and their interactions. In this context, the results obtained in two different EBM modeling, including both only individual metabolite features and their interaction effects, were discussed. The most important predictors included creatinine, nicotinamide, and isocitrate. These metabolites are involved in different biological activities, such as energy metabolism, DNA repair, and cellular signaling. The results demonstrate the potential of the combination of metabolomics and the EBM model in constructing reliable and interpretable prediction outputs for AMI. The discussed metabolite biomarkers may assist in early diagnosis, risk assessment, and personalized treatment methods for AMI patients. This study successfully developed a pipeline incorporating extensive data preprocessing and the EBM model to identify potential metabolite biomarkers for predicting AMI. The EBM model, with its ability to incorporate interaction terms, demonstrated satisfactory classification performance and revealed significant metabolite interactions that could be valuable in assessing AMI risk. However, the results obtained from this study should be validated with studies to be carried out in larger and well-defined samples.
CTA Imaging of Peripheral Arterial Injuries
Authors: Tamburrini Stefania , Lassandro Giulia , Tiralongo Francesco , Iacobellis Francesca , Ronza Francesco Michele , Liguori Carlo , Comune Rosita , Pezzullo Filomena , Galluzzo Michele , Masala Salvatore , Granata Vincenza , Basile Antonio , Scaglione Mariano
Published: June 2024

Traumatic vascular injuries consist of direct or indirect damage to arteries and/or veins and account for 3% of all traumatic injuries. Typical consequences are hemorrhage and ischemia. Vascular injuries of the extremities can occur isolated or in association with major trauma and other organ injuries. They account for 1–2% of patients admitted to emergency departments and for approximately 50% of all arterial injuries. Lower extremities are more frequently injured than upper ones in the adult population. The outcome of vascular injuries is strictly correlated to the environment and the time background. Treatment can be challenging, notably in polytrauma because of the dilemma of which injury should be prioritized, and treatment delay can cause disability or even death, especially for limb vascular injury. Our purposes are to discuss the role of computed tomography angiography (CTA) in the diagnosis of vascular trauma and its optimized protocol to achieve a definitive diagnosis and to assess the radiological signs of vascular injuries and the possible pitfalls.
A Synthetic Small Molecule, LGM2605: A Promising Modulator of Increased Pro-Inflammatory Cytokine and Osteoclast Differentiation by Aggregatibacter actinomycetemcomitans Cytolethal Distending Toxin
Authors: Kim Taewan J. , MacElroy Andrew S. , Defreitas Aleena , Shenker Bruce J. , Boesze-Battaglia Kathleen
Published: June 2024

Our research explores the interplay between Aggregatibacter actinomycetemcomitans (Aa) cytolethal distending toxin (Cdt) and the host’s inflammatory response in molar/incisor pattern periodontitis (MIPP). Cdt disrupts phosphatidylinositol-3,4,5-triphosphate (PIP3) signaling, influencing cytokine expression through canonical and non-canonical inflammasome activation as well as nuclear factor-κB (NF-κB) activation, leading to inflammation in MIPP. THP-1 differentiated macrophages (TDMs) exposed to Cdt exhibited an upregulation of pro-inflammatory genes and subsequent cytokine release. We analyzed the ability of a small molecule therapeutic, LGM2605, known for its anti-inflammatory properties, to reduce pro-inflammatory gene expression and cytokine release in Cdt-exposed and Aa-inoculated TDMs. LGM2605’s mechanism of action involves inhibiting NF-κB while activating the Nrf2–transcription factor and antioxidants. Herein, we show that this small molecule therapeutic mitigates Cdt-induced pro-inflammatory cytokine expression and secretion. Our study also further defines Cdt’s impact on osteoclast differentiation and maturation in MIPP. Cdt promotes increased TRAP+ cells, indicating heightened osteoclast differentiation, specific to Cdt’s phosphatase activity. Cathepsin K levels rise during this process, reflecting changes in TRAP distribution between control and Cdt-treated cells. Exploring LGM2605’s effect on Cdt-induced osteoclast differentiation and maturation, we found TRAP+ cells significantly reduced with LGM2605 treatment compared to Cdt alone. Upon LGM2605 treatment, immunocytochemistry revealed a decreased TRAP intensity and number of multinucleated cells. Moreover, immunoblotting showed reduced TRAP and cathepsin K levels, suggesting LGM2605’s potential to curb osteoclast differentiation and maturation by modulating inflammatory cytokines, possibly involving Nrf2 activation. In summary, our research reveals the intricate connections between Cdt, pro-inflammatory cytokines, and osteoclast differentiation, offering novel therapeutic possibilities for managing these conditions.
Path Planning for Unmanned Aerial Vehicles in Complex Environments
Authors: Gómez Arnaldo César , Zamarreño Suárez María , Pérez Moreno Francisco , Delgado-Aguilera Jurado Raquel
Published: June 2024

This paper introduces a comprehensive framework for generating obstacle-free flight paths for unmanned aerial vehicles (UAVs) in intricate 3D environments. The system leverages the Rapidly Exploring Random Tree (RRT) algorithm to design trajectories that effectively avoid collisions with structures of diverse shapes and sizes. Discussion revolves around the challenges encountered during development and the successful achievement of generating collision-free routes. While the system represents an initial iteration, it serves as a foundation for future projects aiming to refine and expand upon its capabilities. Future work includes simulation testing and integration into UAV missions for image acquisition and structure scanning. Additionally, considerations for swarm deployment and 3D reconstruction using various sensor combinations are outlined. This research contributes to the advancement of autonomous UAV navigation in real-world scenarios.
Lightlore: An Adaptation Framework for Design and Development of xAPI-Based Adaptive Context-Aware Learning Environments
Authors: Hasanov Aziz , Laine Teemu H. , Kim Jongik , Chung Tae-Sun
Published: June 2024

The age of pervasive computing has initiated a boom in the development of adaptive context-aware learning environments (ACALEs), i.e., systems that are capable of detecting a learner’s context and providing adaptive learning services based on this context. Many of the existing educational systems were developed as standalone applications for specific or a small range of adaptive educational scenarios. It would be extremely helpful for developers and educators to have a unified framework that provides an infrastructure for the development of ACALEs. In this study, we propose Lightlore—an adaptation framework that enables the development of different types of ACELEs for a wide range of learning scenarios in formal and informal settings. We first used scenario-based design (SBD) as the design methodology for creating a conceptual model of Lightlore. Educational scenarios were adopted from the results of a previous literature review. We then developed a proof-of-concept implementation of Lightlore, with a hypermedia system for learning data structures that uses the adaptation service of Lightlore. This implementation is essentially an adaptation infrastructure and a programming API for creating new (or transforming existing) adaptive and context-aware educational services. It exploits the experience API (xAPI), a modern e-learning standard and learning record store, thus making coupling with existing learning environments easier. We expect that diverse types of users will benefit from using Lightlore, such as learners, educators, learning environment developers, and researchers on educational technologies.
Raising the Bar on Acceptability Judgments Classification: An Experiment on ItaCoLA Using ELECTRA
Authors: Guarasci Raffaele , Minutolo Aniello , Buonaiuto Giuseppe , De Pietro Giuseppe , Esposito Massimo
Published: June 2024

The task of automatically evaluating acceptability judgments has relished increasing success in Natural Language Processing, starting from including the Corpus of Linguistic Acceptability (CoLa) in the GLUE benchmark dataset. CoLa spawned a thread that led to the development of several similar datasets in different languages, broadening the investigation possibilities to many languages other than English. In this study, leveraging the Italian Corpus of Linguistic Acceptability (ItaCoLA), comprising nearly 10,000 sentences with acceptability judgments, we propose a new methodology that utilizes the neural language model ELECTRA. This approach exceeds the scores obtained from current baselines and demonstrates that it can overcome language-specific limitations in dealing with specific phenomena.
Adaptive Sliding Window–Dynamic Time Warping-Based Fluctuation Series Prediction for the Capacity of Lithium-Ion Batteries
Authors: Sun Sihan , Gu Minming , Liu Tuoqi
Published: June 2024

Accurately predicting the capacity of lithium-ion batteries is crucial for improving battery reliability and preventing potential incidents. Current prediction models for predicting lithium-ion battery capacity fluctuations encounter challenges like inadequate fitting and suboptimal computational efficiency. This study presents a new approach for fluctuation prediction termed ASW-DTW, which integrates Adaptive Sliding Window (ASW) and Dynamic Time Warping (DTW). Initially, this approach leverages Empirical Mode Decomposition (EMD) to preprocess the raw battery capacity data and extract local fluctuation components. Subsequent to this, DTW is employed to forecast the fluctuation sequence through pattern-matching methods. Additionally, to boost model precision and versatility, a feature recognition-based ASW technique is used to determine the optimal window size for the current segment and assist in DTW-based predictions. The study concludes with capacity fluctuation prediction experiments carried out across various lithium-ion battery models. The results demonstrate the efficacy and extensive applicability of the proposed method.
Artificial Neural Network-Based Real-Time Power Management for a Hybrid Renewable Source Applied for a Water Desalination System
Authors: Zgalmi Abir , Ben Rhouma Amine , Belhadj Jamel
Published: June 2024

Water desalination systems integrated with stand-alone hybrid energy sources offer a remarkable solution to the water–energy challenge. Given the complexity of these systems, selecting an appropriate energy management system is crucial. In this regard, employing artificial intelligence techniques to develop and validate an energy management system can be an effective approach for handling such intricate systems. Therefore, this paper presents an ANN-based energy management system (ANNEMS) for a pumping and desalination system connected to an isolated hybrid renewable energy source. Thus, a parametric sensitivity algorithm was developed to identify the optimal neural network architecture. The water–energy management-based supervised multi-layer perceptron neural network demonstrated effective power sharing within a short time frame, achieving accuracy criteria of RMSE, R, and R² between the actual and estimated electrical power of the three motor pumps. The ANNEMS is defined to facilitate real-time power sharing distribution among the various system motor pumps on the test bench, considering the generated power profile and water tank levels. The proposed strategy employs power field oriented control to maintain DC bus voltage stability. Experimental results from the implementation of the proposed ANNEMS are provided. Therein, the power levels of the three motor pumps demonstrated consistent adherence to their reference values. In summary, this study highlights the significance of selecting appropriate energy management for real-time experimental validation.
A Trusted Execution Environment RISC-V System-on-Chip Compatible with Transport Layer Security 1.3
Authors: Kieu-Do-Nguyen Binh , Nguyen Khai-Duy , Dang Tuan-Kiet , The Binh Nguyen , Pham-Quoc Cuong , Tran Ngoc-Thinh , Pham Cong-Kha , Hoang Trong-Thuc
Published: June 2024

The Trusted Execution Environment (TEE) is designed to establish a safe environment that prevents the execution of unauthenticated programs. The nature of TEE is a continuous verification process with hashing, signing, and verifying. Such a process is called the Chain-of-Trust, derived from the Root-of-Trust (RoT). Typically, the RoT is pre-programmed, hard-coded, or embedded in hardware, which is locally produced and checked before booting. The TEE employs various cryptographic processes throughout the boot process to verify the authenticity of the bootloader. It also validates other sensitive data and applications, such as software connected to the operating system. TEE is a self-contained environment and should not serve as the RoT or handle secure boot operations. Therefore, the issue of implementing hardware for RoT has become a challenge that requires further investigation and advancement. The main objective of this proposal is to introduce a secured RISC-V-based System-on-Chip (SoC) architecture capable of securely booting a TEE using a versatile boot program while maintaining complete isolation from the TEE processors. The suggested design has many cryptographic accelerators essential for the secure boot procedure. Furthermore, a separate 32-bit MicroController Unit (MCU) is concealed from the TEE side. This MCU manages sensitive information, such as the root key, and critical operations like the Zero Stage BootLoader (ZSBL) and key generation program. Once the RoT is integrated into the isolated sub-system, it becomes completely unavailable from the TEE side, even after booting, using any method. Besides providing a secured boot flow, the system is integrated with essential crypto-cores supporting Transport Layer Security (TLS) 1.3. The chip is finally fabricated using the Complementary Metal–Oxide–Semiconductor (CMOS) 180 nm process.
Are Your Comments Positive? A Self-Distillation Contrastive Learning Method for Analyzing Online Public Opinion
Authors: Zhou Dongyang , Shi Lida , Wang Bo , Xu Hao , Huang Wei
Published: June 2024

With the popularity of social media, online opinion analysis is becoming more and more widely and deeply used in management studies. Automatically recognizing the sentiment of user reviews is a crucial tool for opinion analysis research. However, previous studies mainly have focused on specific scenarios or algorithms that cannot be directly applied to real-world opinion analysis. To address this issue, we collect a new dataset of user reviews from multiple real-world scenarios such as e-retail, e-commerce, movie reviews, and social media. Due to the heterogeneity and complexity of this multi-scenario review data, we propose a self-distillation contrastive learning method. Specifically, we utilize two EMA (exponential moving average) models to generate soft labels as additional supervision. Additionally, we introduce the prototypical supervised contrastive learning module to reduce the variability of data in different scenarios by pulling in representations of the same class. Our method has proven to be extremely competitive, outperforming other advanced methods. Specifically, our method achieves an 87.44% F1 score, exceeding the performance of current advanced methods by 1.07%. Experimental results, including examples and visualization analysis, further demonstrate the superiority of our method.
The Effect of Knife Wear and Sharpening Mode on Chipper Productivity and Delays
Authors: Mihelič Matevž , Vusić Dinko , Ursić Branko , Zadro Antonio , Spinelli Raffaele
Published: June 2024

The production of wood chips can be achieved using different types of wood chippers whose productivity can be influenced by many factors including proper knife management. Research was conducted to determine the productivity of the new Diamant chipper in chipping air-dried tops stacked at a roadside landing and to compare the efficiency of dry sharpening and wet sharpening in restoring chipper productivity, the time required by dry sharpening with that of knife replacement, and the cost of dry sharpening to knife change in real-life conditions. To clearly define the influence of knife management, a model of the effect of knife wear on chipper productivity was produced. Analysis of variance was used to check the significance of any differences in chipping and total time consumption per cycle. Multiple regression was used to express the relationship between chipping time consumption per cycle and the cumulated mass processed by a set of knives—the latter taken as an indicator of knife wear. The study lasted 10 full workdays, included a total of 136 truckloads or 3560 t of fresh wood chips (or green tons = gt), and resulted in the average productivity of 59.0 gt per productive chipping hour (excluding all delays) or 39.4 gt per machine scheduled hour (including all delays). Delays represented 37% of total worksite time. Knife management (dry sharpening or change) accounted for 30% of the total delay time due to raw material contamination. Dry sharpening took 30% less time than a full knife change. As wear accumulated and knives lost their edge, the chipping time per cycle increased from 25 in the first cycle (full truck load) to 38 min in the third cycle. The presented study offers robust productivity figures, together with a reliable estimate of the productivity losses caused by knife wear, and could help improve knife management in order to increase chipper productivity as well as reduce unnecessary delays.
Wildfire Effects on the Soil Respiration and Bacterial Microbiota Composition in Mediterranean-Type Ecosystems
Authors: Dalias Panagiotis , Hadjisterkotis Eleftherios , Omirou Michalis , Michaelidou Ourania , Ioannides Ioannis M. , Neocleous Damianos , Christou Anastasis
Published: June 2024

This work provides insights into the effect of fire on soil processes in Mediterranean-type ecosystems in Cyprus. Soil samples from mountainous sites that were subjected to a summer wildfire and adjacent control samples were collected. Incubations were used to estimate basal respiration and isolate soil CO2 release of heterotrophic microorganisms from autotrophic root respiration and heterotrophic respiration from litter decomposition. Physicochemical property changes, bacteria community changes using DNA extraction and 16S rRNA gene analysis, and the effects of ash and fresh litter addition were studied to reveal the microbial composition and the post-fire soil function. Laboratory incubation showed that burned soils constantly showed higher microbial respiration rates compared with control unburned areas, even six months after a fire. Adding ash to unburned samples increased microbial respiration, suggesting that increased nutrient availability positively corelates with the increased release of CO2 from fire-affected soil. Elevated temperatures due to the wildfire exerted significant effects on the composition of soil bacterial microbiota. Nevertheless, the wildfire did not affect the alpha-diversity of soil bacteria. New communities of microorganisms are still able to decompose fresh plant material after a fire, but at a slower rate than natural pre-fire populations.
Experimental Investigation into Shockwave and Flame Characteristics of Hydrogen Released through Various Pressure Relief Devices
Authors: Kuang Chen , Nie Shishuai , Lin Yujie , Liu Di , Ling Xiaodong , Chen Guoxin , Liu Yi , Yu Anfeng
Published: June 2024

This paper presents an experimental investigation into shockwave and flame characteristics of compressed hydrogen released through various types of pressure relief devices (PRDs) for which data have not been previously reported. Burst disks and safety valves with different set pressure (P0) of 22–140 MPa were tested. Shockwave intensity/velocity spontaneous ignition/flame behavior was monitored by in-situ pressure/light sensors, respectively. Previous works have mostly focused on burst disks with low P0 (under 10 MPa), leaving safety valves and high-pressure burst disks uninvestigated. It was found that shockwave/spontaneous ignition behavior differs with PRD types. Spontaneous ignition occurs in all burst disk cases, along with an ignition/self-extinguishment/reignition process with relatively low P0, which has not been revealed previously. In contrast, none of the safety valves cause spontaneous ignition, resulting from the absence of shockwave due to lower overpressure values/rise rate during release. This suggests that shockwave formed by sudden release is the most dominant factor in spontaneous ignition. Also, the occurrence of self-extinguishment does not guarantee the absence of jet flame. This work provides a comprehensive database revealing shockwave and flame characteristics of hydrogen released through different PRDs, which offers basic data and theoretical support for the safety and risk assessment of high-pressure hydrogen facilities.
Evaluating the Combustion Performance of the Usual Timbers in Furniture Using a Grey Correlation Method Based on Thermolysis, Ignition, and Flame Spread
Authors: Yu Zhijin , Song Jiani , Xu Lan , Zhang Hao
Published: June 2024

Timber is the most widely used material for furniture in view of its characteristics of light mass, high strength, easy processing, coloring, and decorative appearance. However, the flammability of wood has been frequently associated with increased fire intensity and the rapid spread of fire in buildings. In this paper, the combustion performance of six kinds of common furniture timber was investigated based on thermogravimetric analysis at 25–500 °C, cone calorimetry with 50 kW/m2 thermal radiation intensity, and flame spread experiments with 3 kW/m2 thermal radiation intensity. The ignition, weight loss, thermogenesis, smoke, and flame spread characteristics of these timbers were obtained. Subsequently, a comprehensive index system including thermal stability, heat release ability, smoke production capacity, and flame spreading speed was constructed to evaluate the combustion performance of the selected timbers. In addition, a grey correlation method relying on the game theory to assign weight was proposed for the quantitative analysis of the relevant evaluation indexes. As a result, the combustion performance of the six kinds of timber, which was defined as a specific value from poor to good, was as follows: pine (0.8696) > Chinese fir (0.8568) > Oriented Strandboard (OSB) (0.8425) > density board (0.8122) > plywood (0.8087) > elm (0.7909). Timber with poor combustion performance contributes to the reduction in fire risk in buildings. Our suggestions are of great significance for selecting furniture timber from the perspective of the prevention and control of building fires.
Enzyme Cascade Amplification-Based Immunoassay Using Alkaline Phosphatase-Linked Single-Chain Variable Fragment Fusion Tracer and MnO2 Nanosheets for Detection of Deoxynivalenol in Corn Samples
Authors: Xie Guifang , Mao Fujing , Huang Yirui , Wen Li , Sun Zhichang , He Zhenyun , Liu Xing
Published: June 2024

Deoxynivalenol (DON) is a common mycotoxin that contaminates cereals. Therefore, the development of sensitive and efficient detection methods for DON is essential to guarantee food safety and human health. In this study, an enzyme cascade amplification-based immunoassay (ECAIA) using a dual-functional alkaline phosphatase-linked single-chain fragment variable fusion tracer (scFv-ALP) and MnO2 nanosheets was established for DON detection. The scFv-ALP effectively catalyzes the hydrolysis of ascorbyl-2-phosphate (AAP) to produce ascorbic acid (AA). This AA subsequently interacts with MnO2 nanosheets to initiate a redox reaction that results in the loss of oxidizing properties of MnO2. In the absence of ALP, MnO2 nanosheets can oxidize 3,3′,5,5′-tetramethylbenzidine (TMB) to produce the blue oxidized product of TMB, which exhibits a signal at a wavelength of 650 nm for quantitative analysis. After optimization, the ECAIA had a limit of detection of 0.45 ng/mL and a linear range of 1.2–35.41 ng/mL. The ECAIA exhibited good accuracy in recovery experiments and high selectivity for DON. Moreover, the detection results of the actual corn samples correlated well with those from high-performance liquid chromatography. Overall, the proposed ECAIA based on the scFv-ALP and MnO2 nanosheets was demonstrated as a reliable tool for the detection of DON in corn samples.
Effect of Essential Oils and Dried Herbs on the Shelf Life of Fresh Goat Lump Cheese
Authors: Kačániová Miroslava , Joanidis Patrícia , Lakatošová Jana , Kunová Simona , Benešová Lucia , Ikromi Khurshed , Akhmedov Farkhod , Boboev Khayyol , Gulmahmad Mirzozoda , Niyatbekzoda Fariza , Toshkhodjaev Nasimjon , Bobokalonov Farkhod , Kamolov Nasimdzhon , Čmiková Natália
Published: June 2024

In recent years, the use of natural preservatives in food products has gained significant attention due to their potential health benefits and effectiveness. A standardized microbiological analysis was conducted on Slovak farm-produced lump goat cheese samples to determine the antibacterial activity of dry herbs and essential oils added to vacuum-packed goat cheese. We employed five dried herbs and five essential oils derived from the same plants. The microbiological quality of 145 fresh and vacuum-packed goat cheese samples was assessed. The number of coliform bacteria, total viable count, lactic acid bacteria, and microscopic filamentous fungi were examined in raw cheese samples stored for 12 days at 4 °C. All cheese samples were vacuum-packed (control samples were packed without vacuum). This study evaluated the potential benefits of using essential oils and dried herbs from thyme (Thymus serpyllum L.), black pepper (Piper nigrum L.), clove (Eugenia caryophyllus Thunb.), mint (Mentha × piperita L.), and basil (Ocimum basilicum L.) as preservatives. The essential oils were obtained from Hanus Ltd., Nitra, Slovakia, and were applied at a concentration of 2%. The dried herbs were obtained from Popradský čaj (Poprad, Slovakia) and Mäspoma Ltd. (Zvolen, Slovakia). The results showed that all microorganism groups were significantly reduced in cheese samples following the application of essential oils throughout the entire storage period. During the preservation of cheese samples in polyethylene bags used for vacuum packing food, Lactococcus garvieae, L. lactis, Enterobacter cloacae, and Serratia liquefaciens were the most frequently isolated microbiota. Essential oils and dried herbs demonstrated antimicrobial potential during the storage of vacuum-packed goat cheese.
Study of Factors Influencing the Oral Bioaccessibility of Commonly Used and Detected Pesticides in Bananas and Mangoes Based on in vitro Methods
Authors: Ma Chen , Zhang Qun , Lv Dai-Zhu , Song Jia , Fan Qiong , Tian Hai , Wang Ming-Yue
Published: June 2024

Estimating the impact of pesticide residue bioaccessibility in fruits on dietary exposure is a complex task in human health risk assessment. This research investigated the bioaccessibility of ten commonly used and detected pesticides in bananas and mangoes, as well as the factors influencing it, using an in vitro model. The highest bioaccessibility was observed at pH levels of 2.5 and 6.5 in the gastric and intestinal stages, respectively. Bioaccessibility decreased significantly with increasing solid/liquid ratios for most pesticides. The consumption of protein and four dietary components (carbohydrates, protein, lipids, and dietary fiber) could significantly reduce pesticide bioaccessibility by 9.89–48.32% (p < 0.05). Bioaccessibility in oral and gastric stages among four populations followed the order of adults/the elderly > children > infants, due to decreasing concentrations of α-amylase and pepsin. Pesticides in bananas generally exhibited a higher bioaccessibility (18.65–82.97%) compared to that in mangoes (11.68–87.57%). Bioaccessibility showed a negative correlation with the Log P values of the target pesticide, while no clear relationship was found between bioaccessibility and initial pesticide concentrations. Incorporating bioaccessible pesticide concentrations into risk assessments could lower dietary risk estimates by 11.85–79.57%. Assessing human exposure to pesticides based on bioaccessibility would greatly improve the accuracy of the risk assessment.
Effects of Inoculating Autochthonous Starter Cultures on Changes of N-Nitrosamines and Their Precursors in Chinese Traditional Fermented Fish during In Vitro Human Digestion
Authors: Li Han , Li Qian , Wang Qi , Chen Jiwang , Xia Wenshui , Liao E
Published: June 2024

The objective of this research was to investigate the impact of inoculating autochthonous starter cultures on the alterations in microorganisms, biogenic amines, nitrite, and N-nitrosamines in Chinese traditional fermented fish products (CTFPs) during in vitro human digestion. The results revealed that gastric digestion significantly (p < 0.05) inhibited the proliferation of lactic acid bacteria, yeast, Staphylococcus, and Enterobacteriaceae, whereas various microorganisms proliferated extensively during small intestine digestion. Meanwhile, small intestine digestion could significantly increase (p < 0.05) levels of putrescine, cadaverine, and tyramine. The reduced content observed in inoculated fermentation groups suggests that starter cultures may have the ability to deplete biogenic amines in this digestion stage. Gastric digestion significantly (p < 0.05) inhibited nitrite accumulation in all CTFPs samples. Conversely, the nitrite content increased significantly (p < 0.05) in all groups during subsequent small intestine digestion. However, the rise in the inoculated fermentation groups was smaller than that observed in the spontaneous fermentation group, indicating a potentially positive role of inoculated fermentation in inhibiting nitrite accumulation during this phase. Additionally, gastric digestion significantly (p < 0.05) elevated the levels of N-nitrosodimethylamine (NDMA) and N-nitrosopiperidine in CTFPs. Inoculation with L. plantarum 120, S. cerevisiae 2018, and mixed starter cultures (L. plantarum 120, S. cerevisiae 2018, and S. xylosus 135 [1:1:1]) effectively increased the degree of depletion of NDMA during this digestion process.
Fatty Acids in Dry Beans (Phaseolus vulgaris L.): A Contribution to Their Analysis and the Characterization of a Diversity Panel
Authors: Rodríguez Madrera Roberto , Campa Negrillo Ana , Ferreira Fernández Juan José
Published: June 2024

Dry bean (Phaseolus vulgaris L.) is a crop of high nutritional interest widespread throughout the world. This research had two objectives. On the one hand, the development and validation of an analytical method to quantify fatty acids in dry beans based on the extraction and derivatization in a single step and later quantification by gas chromatography. On the other, its application to characterize the fatty acid content in a diversity panel consisting of 172 lines. The method was successfully validated in terms of accuracy, precision and robustness. Among the 14 fatty acids that constitute the fatty acid profile of dry bean, the most quantitatively important were linolenic acid, the major fatty acid in all cases, with an average value of 6.7 mg/g, followed by linoleic acid (3.9 mg/g), palmitic acid (2.9 mg/g) and oleic acid (1.5 mg/g). The concentrations of fatty acids in dry bean were influenced by the gene pool, with the Mesoamerican gene pool showing a higher content of palmitic, stearic, linoleic and linolenic acids and the Andean gene pool a higher level of cis-vaccenic acid. Also, the expression of fatty acid content showed high heritability. The information generated constitutes a robust database of interest in food technology, nutrition and breeding programs.
Some New Approaches to Fractional Euler–Maclaurin-Type Inequalities via Various Function Classes
Authors: Gümüş Mehmet , Hezenci Fatih , Budak Hüseyin
Published: June 2024

This paper aims to examine an approach that studies many Euler–Maclaurin-type inequalities for various function classes applying Riemann–Liouville fractional integrals. Afterwards, our results are provided by using special cases of obtained theorems and examples. Moreover, several Euler–Maclaurin-type inequalities are presented for bounded functions by fractional integrals. Some fractional Euler–Maclaurin-type inequalities are established for Lipschitzian functions. Finally, several Euler–Maclaurin-type inequalities are constructed by fractional integrals of bounded variation.
Fractal Dimension Analysis of Pore Throat Structure in Tight Sandstone Reservoirs of Huagang Formation: Jiaxing Area of East China Sea Basin
Authors: Wang Wenguang , Lin Chengyan , Zhang Xianguo
Published: June 2024

The reservoir quality of tight sandstone is usually affected by pore throat structures, and understanding pore throat structures and their fractal characteristics is crucial for the exploration and development of tight sandstone gas. In this study, fractal dimensions of pore throat structures and the effect of diagenesis on the fractal dimension of tight sandstone sweet spot in Huagang Formation, Jiaxing area, East China Sea Basin were studied by means of thin sections, scanning electron microscopes, X-ray diffraction analysis, scanning electron microscope quantitative mineral evaluation, and high pressure mercury injection experiments. The results show that the total fractal dimension ranges of type I, type II, and type III sweet spots were 2.62–2.87, 2.22–2.56, and 2.71–2.77, respectively. The negative correlation between total fractal dimensions, porosity, and permeability of type I sweet spots was different from those of type II and type III sweet spots. The negative correlation between total fractal dimensions of type II and type III sweet spots and maximum mercury saturation, average pore throat radius, and skewness were significant, whereas the correlation between total fractal dimensions of type I sweet spots, and maximum mercury saturation, average pore throat radius and skewness were not significant. The positive correlation between the total fractal dimensions of type II and type III sweet spots and the relative sorting coefficient, displacement pressure, and efficiency of mercury withdrawal were significant, whereas the correlation between the total fractal dimension of type I sweet spots and relative sorting coefficients, displacement pressures and efficiency of mercury withdrawal were not significant. The effect of diagenesis on fractal dimensions was investigated. Compaction reduced the pore space of tight sandstone and increased fractal dimensions. Quartz cementation and calcite cementation blocked pores and throats, reduced pore space, and increased fractal dimensions. Chlorite coat can inhibit compaction, protect pore throat structures, and maintain fractal dimensions. Most clay minerals filled primary pores and secondary pores and increased fractal dimensions. Dissolution increased the pore space of tight sandstone and decreased the fractal dimensions of the pore throat structures. The pore throat structures of type I sweet spots were mainly composed of macropores, mesopores, transitional pores, and micropores, and the fractal dimension of type I sweet spots was chiefly controlled by chlorite coat formation, dissolution, and a small amount of compaction. This study provides a reference for pore throat structure and fractal dimension analysis of tight sandstone sweet spots.
Method for Measuring the Fractional Derivative of a Two-Dimensional Magnetic Field Based on Taylor–Riemann Series
Authors: Wang Ruijian , Sui Yangyi
Published: June 2024

In magnetic data processing, a fractional derivative can enhance details without excessively amplifying high-frequency noise. To obtain a fractional derivative numerically, a large number of survey points are required. This article demonstrates how a few survey points can be used to obtain the fractional derivative of a two-dimensional magnetic field through the application of Taylor–Riemann series. First, we derive the measurement method for the fractional gradient. This method is achieved by measuring the magnetic field at several survey points on a circle, then constructing analytical functions and finally calculating the fractional derivative. Next, an experiment is designed and simulated to demonstrate the impact of the fractional derivative start point and the ability to suppress Gaussian noise. Finally, the experiment is performed, which verifies the feasibility of the proposed method in a two-dimensional magnetic field.
Influence of Sonication on the Molecular Characteristics of Carbopol® and Its Rheological Behavior in Microgels
Authors: Pérez-González José , Muñoz-Castro Yusef , Rodríguez-González Francisco , Marín-Santibáñez Benjamín M. , Medina-Bañuelos Esteban F.
Published: June 2024

In this work, the effect of sonication on the molecular characteristics of polyacrylic acid (Carbopol® Ultrez 10), as well as on its rheological behavior in aqueous dispersions and microgels, was analyzed for the first time by rheometry, weight-average molecular weight (Mw) measurements via static light scattering (SLS), Fourier transform infrared (FTIR) spectroscopy and confocal microscopy. For this, the precursor dispersion and the microgels containing 0.25 wt.% of Ultrez 10 were sonicated in a commercial ultrasound bath at constant power and at different times. The main rheological properties of the microgel, namely, shear modulus, yield stress and viscosity, all decreased with increasing sonication time, while the microgel’s Herschel–Bulkley (H-B) behavior, without thixotropy, was preserved. Also, Mw of Ultrez 10 decreased up to almost one-third (109,212 g/mol) of its original value (300,860 g/mol) after 180 min of sonication. These results evidence a softening of the gel microstructure, which results from the reduction in the Mw of polyacrylic acid with sonication time. Separately, FTIR measurements show that sonication produces scission in the C-C links of the Carbopol® backbone, which results in chains with the same chemistry but lower molecular weight. Finally, confocal microscopy observations revealed a diminution of the size of the microsponge domains and more free solvent with sonication time, which is reflected in a less compact and softer microstructure. The present results indicate that both the microstructure and the rheological behavior of Carbopol® microgels, in particular, and complex fluids, in general, may be manipulated or tailored by systematic high-power ultrasonication.
Fabrication and Characterization of Porous PEGDA Hydrogels for Articular Cartilage Regeneration
Authors: Gonella Silvia , Domingues Margarida F. , Miguel Filipe , Moura Carla S. , Rodrigues Carlos A. V. , Ferreira Frederico Castelo , Silva João C.
Published: June 2024

Functional articular cartilage regeneration remains an unmet medical challenge, increasing the interest for innovative biomaterial-based tissue engineering (TE) strategies. Hydrogels, 3D macromolecular networks with hydrophilic groups, present articular cartilage-like features such as high water content and load-bearing capacity. In this study, 3D porous polyethylene glycol diacrylate (PEGDA) hydrogels were fabricated combining the gas foaming technique and a UV-based crosslinking strategy. The 3D porous PEGDA hydrogels were characterized in terms of their physical, structural and mechanical properties. Our results showed that the size of the hydrogel pores can be modulated by varying the initiator concentration. In vitro cytotoxicity tests showed that 3D porous PEGDA hydrogels presented high biocompatibility both with human chondrocytes and osteoblast-like cells. Importantly, the 3D porous PEGDA hydrogels supported the viability and chondrogenic differentiation of human bone marrow-derived mesenchymal stem/stromal cell (hBM-MSC)-based spheroids as demonstrated by the positive staining of typical cartilage extracellular matrix (ECM) (glycosaminoglycans (GAGs)) and upregulation of chondrogenesis marker genes. Overall, the produced 3D porous PEGDA hydrogels presented cartilage-like mechanical properties and supported MSC spheroid chondrogenesis, highlighting their potential as suitable scaffolds for cartilage TE or disease modelling strategies.
Redesigned Electrodes for Improved Intraoperative Nerve Conduction Studies during the Treatment of Peripheral Nerve Injuries
Authors: Riemann Nathaniel , Coursen Jack , Porras Laura Elena , Sabogal Bryan , Liang Xin-Hua , Guaraca Christian , Belzberg Allan , Ringkamp Matthias , Wu Gang , Zhu Lily , Weed Samantha , Miranda Constanza
Published: June 2024

Traumatic peripheral nerve injuries (PNI), present with symptoms ranging from pain to loss of motor and sensory function. Difficulties in intraoperative visual assessment of nerve functional status necessitate intraoperative nerve conduction studies (INCSs) by neurosurgeons and neurologists to determine the presence of functioning axons in the zone of a PNI. This process, also referred to as nerve “inching”, uses a set of stimulating and recording electrode hooks to lift the injured nerve from the surrounding surgical field and to determine whether an electrical stimulus can travel through the zone of injury. However, confounding electrical signal artifacts can arise from the current workflow and electrode design, particularly from the mandatory lifting of the nerve, complicating the definitive assessment of nerve function and neurosurgical treatment decision-making. The objective of this study is to describe the design process and verification testing of our group’s newly designed stimulating and recording electrodes that do not require the lifting or displacement of the injured nerve during INCSs. Ergonomic in vivo analysis of the device within a porcine model demonstrated successful intraoperative manipulation of the device, while quantitative nerve action potential (NAP) signal analysis with an ex vivo simulated “inching” procedure on healthy non-human primate nerve tissue demonstrated excellent reproducible recorded NAP fidelity and the absence of NAP signal artifacts at all points of recording. Lastly, electrode pullout force testing determined maximum forces of 0.43 N, 1.57 N, and 3.61 N required to remove the device from 2 mm, 5 mm, and 1 cm nerve models, respectively, which are well within established thresholds for nerve safety. These results suggest that these new electrodes can safely and successfully perform accurate PNI assessment without the presence of artifacts, with the potential to improve the INCS standard of care while remaining compatible with currently used neurosurgical technology, infrastructure, and clinical workflows.
Predictors of Perceived Healthcare Professionals’ Well-Being in Work Design: A Cross-Sectional Study with Multigroup PLS Structural Equation Modeling
Authors: Nesic Danijela , Slavkovic Marko , Zdravkovic Nebojsa , Jerkan Nikola
Published: June 2024

The growing attention paid to employee well-being in the healthcare system, along with the reinforcement of factors that drive change in the work environment, provides a strong foundation for studying the relationship between work design and its related outcomes. The aim of this study was to examine the relationship between the elements of work design and the overall well-being of healthcare professionals, particularly the differences across multiple groups of various genders and age brackets. A cross-sectional study design was employed, and a convenience sampling method was used. The study participants were healthcare professionals, and a total of 427 valid surveys were collected. The partial least squares structural equation modeling (PLS-SEM) approach was deployed to test the relationship between the determinants of work design and the perceived well-being of healthcare professionals. The results indicate a strong positive relationship between the social characteristics of work and overall well-being, as well as a positive contribution of the work context to the perceived well-being of healthcare professionals. The findings validated that knowledge characteristics had no beneficial impacts on overall well-being; nor did the task characteristics of work design. Although the mentioned associations failed to demonstrate statistical significance, the results nonetheless have significant practical consequences that are comparable to those of the relationships that demonstrated statistical significance.
Advancements in Molecular Mechanism Research on Bolting Traits in Vegetable Crops
Authors: Tan Guo-Fei , Luo Qing , Zhu Shun-Hua , Zhong Xiu-Lai , Meng Ping-Hong , Li Meng-Yao , Chen Zhi-Feng , Xiong Ai-Sheng
Published: June 2024

Bolting and flowering of vegetables are induced by vernalization in their early growth stage. This phenomenon is called premature bolting, and it has caused massive losses in production of vegetables such as cabbage, celery, carrot, radish, and spinach, etc. This review aimed to summarize studies on bolting and flowering pathways, physiological and biochemical changes, and underlined molecular mechanisms of various vegetable crop bolting involving genome and transcriptome analysis, and its association with vegetable breeding. This review could provide basic knowledge to carry out research on vegetable genetics and breeding and vegetable cultivation.
A Biostimulant Containing Humic and Fulvic Acids Promotes Growth and Health of Tomato ‘Bush Beefsteak’ Plants
Authors: Quijia Pillajo Juan , Chapin Laura J. , Martins Evili Marai , Jones Michelle L.
Published: June 2024

Humic substances are a type of biostimulant used in greenhouse production to promote plant growth and health. Our goal was to evaluate the effect of three commercially available biostimulants containing humic and/or fulvic acids (HumaPro, FulviPro, and Micromate) on the performance and tissue nutrient concentration of Solanum lycopersicum L. ‘Bush Beefsteak’ grown in a peat-based substrate. We conducted four experiments testing application rate and fertility level (50 and 100 mg⋅L–1 N) (Exp. 1), application rate and frequency (Exp. 2), direct Micromate incorporation into the substrate (Exp. 3), and FulviPro application method (drench vs. foliar spray) (Exp. 4). Plants were fertilized with 20N-1.3P-15.7K. Experiments 2, 3, and 4 were conducted under low fertility (50 mg⋅L–1 N). Micromate promoted growth when applied as a weekly drench at 40 g·L−1 or when incorporated into the substrate (20 g in 1 L of the substrate). Micromate-treated plants showed high P, S, and Si in the shoot and root tissues. FulviPro showed a negative effect when applied as a drench at higher rates, but foliar application increased greenness (Green Leaf Index). The negative effects of FulviPro might be due to the over-accumulation of Fe and Mn when applied as a drench.