Background and objectives: Acute myeloid leukemia (AML) is a hematological malignancy characterized by uncontrolled proliferation of immature myeloid cells. Immune checkpoint molecules such as programmed cell death protein 1 (PD-1) and lymphocyte activation gene-3 (LAG-3) are essential for controlling anti-tumor immune responses. This study aims to explore the correlation between specific genetic variations (SNPs) in the PDCD1 (rs2227981) and LAG3 (rs12313899) genes and the likelihood of developing AML in the Saudi population. Material and methods: total of 98 Saudi AML patients and 131 healthy controls were genotyped for the PDCD1 rs2227981 and LAG3 rs12313899 polymorphisms using TaqMan genotyping assays. A logistic regression analysis was conducted to evaluate the relationship between the SNPs and AML risk using several genetic models. Results: The results revealed a significant association between the PDCD1 rs2227981 polymorphism and increased AML risk. In AML patients, the frequency of the G allele was considerably greater than in healthy controls (OR = 1.93, 95% CI: 1.31–2.81, p = 0.00080). The GG and AG genotypes were associated with a very high risk of developing AML (p < 0.0001). In contrast, no significant association was observed between the LAG3 rs12313899 polymorphism and AML risk in the studied population. In silico analysis of gene expression profiles from public databases suggested the potential impact of PDCD1 expression levels on the overall survival of AML patients. Conclusions: This study provides evidence for the association of the PDCD1 rs2227981 polymorphism with an increased risk for AML in the Saudi population. Full article

The aim of this paper is to establish a valid procedure for better understanding all of the phenomena associated with the high-speed machining of glass fiber-reinforced plastic (GFRP) composites. Both rectangular and circular specimens were machined at high cutting speeds (up to 50 m/min) in order to understand what occurred for all values of fiber orientation angles during machining operations. An innovative testing methodology was proposed and studied to investigate the phenomenon of burr formation and thus understand how to avoid it during machining operations. To this end, the forces arising during the machining process and the roughness of the resulting surface were carefully studied and correlated with the cutting angle. Additionally, the cutting surface and chip morphology formed during cutting tests were examined using a high-speed camera. Close correlations were found between the variations in the cutting forces’ signals and the trends of the surface roughness and the morphology of the machined surface. Full article

Phthalate esters (PAEs) are a group of chemicals used to improve the flexibility and durability of plastics. The chemical properties and the resistance to high temperatures promote their degradation and release into the environment. Food and beverages can be contaminated by PAEs through the migration from packaging material because they are not covalently bound to plastic and also via different kinds of environmental sources or during processing. For instance, alcoholic drinks in plastic containers are a particular risk, since the ethanol contained provides a good solubility for PAEs. According to its role as an endocrine disruptor compound and its adverse effects on the liver, kidney, and reproductive and respiratory systems, the International Agency on Research Cancer (IARC) classified di-(2-ethylhexyl) phthalate (DEHP) as a possible human carcinogen. For this reason, to control human exposure to PAEs, many countries prohibited their use in food as non-food substances. For example, in Europe, the Commission Regulation (EU) 2018/2005 restricts the use of DEHP, dibutyl phthalate (DBP), benzyl butyl phthalate (BBP), and diisobutyl phthalate (DiBP) to a concentration equal to or below 0.1 by weight in plasticizers in articles used by consumers or in indoor areas. There are reports from the US Food and Drug Administration (FDA) that some beverages (and food as well), particularly fruit juices, contain high levels of phthalates. In some cases, the deliberate adulteration of soft drinks with phthalate esters has been reported. This paper would like to show the difficulties of performing PAE analysis in beverage matrices, in particular alcoholic beverages, as well as the main solutions provided for quality control in the industrial branches. Full article

Background and Objectives: This study aims to report the location of the placenta in the first trimester of pregnancy in groups of women according to the number of previous caesarean deliveries and the visibility of the caesarean scar niche. Materials and Methods: The prospective observational research included adult women aged 18 to 41 years during pregnancy after one or more previous caesarean sections (CSs). Transvaginal (TVS) and transabdominal sonography (TAS) was used to examine the uterine scar and placental location during 11–14 weeks. The CS scar niche (“defect”) was bordered in the sagittal plane as a notch at the previous CS scar’s site with a depth of 2.0 mm or more. A comparative analysis of the placental location (high or low and anterior or posterior) was performed between groups of women according to the CS number and the CS scar niche. Results: A total of 122 participants were enrolled during the first-trimester screening. The CS scar defect (“niche”) was visible in 40.2% of cases. In cases after one previous CS, the placenta was low in the uterine cavity (anterior or posterior) at 77.4%, and after two or more CSs, it was at 67.9%. Comparing the two groups according to the CS scar niche, the placenta was low in 75.5% of cases in the participant group with a CS scar niche and in 75% of cases without a CS scar niche (p = 0.949). Conclusions: The number of previous caesarean deliveries has no effect on the incidence rate of low-lying placentas in the first trimester. Moreover, the presence of the CS scar niche is not associated with anterior low-lying placentas. Full article

(1) Background: Recent digital workflows are being developed for full-arch rehabilitations supported by implants with immediate function. The purpose of this case series is to describe a new digital workflow for the All-on-4 concept. (2) Methods: The patients were rehabilitated using the All-on-4 concept with a digital workflow including computerized tomography scanning, intra-oral scanning, and CAD-CAM production of the temporary prosthesis, with the 3D printing of stackable guides (base guide, implant guide, and prosthetic guide). The passive fit of the prostheses and the time to perform the rehabilitations were evaluated. (3) Results: The digital workflow allowed for predictable bone reduction, the insertion of implants with immediate function, and the connection of an implant-supported prosthesis with immediate loading. The time registered to perform the full-arch rehabilitations (implant insertion, abutment connection, prosthesis connection) was below 2 hours and 30 min. No passive fit issues were noted. (4) Conclusions: within the limitation of this case series, the digital workflow applied to the All-on-4 concept using stackable base-, implant-, and prosthetic guides constitutes a potential alternative with decreased time for the procedure without prejudice of the outcome. Full article

Purpose: This study aimed to employ the incremental digital image correlation (DIC) method to obtain displacement and strain field data of the cornea from Corvis ST (CVS) sequences and access the performance of embedding these biomechanical data with machine learning models to distinguish forme fruste keratoconus (FFKC) from normal corneas. Methods: 100 subjects were categorized into normal (N = 50) and FFKC (N = 50) groups. Image sequences depicting the horizontal cross-section of the human cornea under air puff were captured using the Corvis ST tonometer. The high-speed evolution of full-field corneal displacement, strain, velocity, and strain rate was reconstructed utilizing the incremental DIC approach. Maximum (max-) and average (ave-) values of full-field displacement V, shear strain γxy, velocity VR, and shear strain rate γxyR were determined over time, generating eight evolution curves denoting max-V, max-γxy, max-VR, max-γxyR, ave-V, ave-γxy, ave-VR, and ave-γxyR, respectively. These evolution data were inputted into two machine learning (ML) models, specifically Naïve Bayes (NB) and Random Forest (RF) models, which were subsequently employed to construct a voting classifier. The performance of the models in diagnosing FFKC from normal corneas was compared to existing CVS parameters. Results: The Normal group and the FFKC group each included 50 eyes. The FFKC group did not differ from healthy controls for age (p = 0.26) and gender (p = 0.36) at baseline, but they had significantly lower bIOP (p < 0.001) and thinner central cornea thickness (CCT) (p < 0.001). The results demonstrated that the proposed voting ensemble model yielded the highest performance with an AUC of 1.00, followed by the RF model with an AUC of 0.99. Radius and A2 Time emerged as the best-performing CVS parameters with AUC values of 0.948 and 0.938, respectively. Nonetheless, no existing Corvis ST parameters outperformed the ML models. A progressive enhancement in performance of the ML models was observed with incremental time points during the corneal deformation. Conclusion: This study represents the first instance where displacement and strain data following incremental DIC analysis of Corvis ST images were integrated with machine learning models to effectively differentiate FFKC corneas from normal ones, achieving superior accuracy compared to existing CVS parameters. Considering biomechanical responses of the inner cornea and their temporal pattern changes may significantly improve the early detection of keratoconus. Full article

We describe 20 datasets derived through signal filtering and feature extraction steps applied to the raw time series EEG data of 20 epileptic patients, as well as the methods we used to derive them. Background: Epilepsy is a complex neurological disorder which has seizures as its hallmark. Electroencephalography plays a crucial role in epilepsy assessment, offering insights into the brain’s electrical activity and advancing our understanding of seizures. The availability of tagged training sets covering all seizure phases—inter-ictal, pre-ictal, ictal, and post-ictal—is crucial for data-driven epilepsy analyses. Methods: Using the sliding window technique with a two-second window length and a one-second time slip, we extract multiple features from the preprocessed EEG time series of 20 patients from the Freiburg Seizure Prediction Database. In addition, we assign a class label to each instance to specify its corresponding seizure phase. All these operations are made through a software application we developed, which is named Training Builder. Results: The 20 tagged training datasets each contain 1080 univariate and bivariate features, and are openly and publicly available. Conclusions: The datasets support the training of data-driven models for seizure detection, prediction, and clustering, based on features engineering. Full article

Fiber–particle-reinforced silica aerogels are widely applied in thermal insulation. Knowing their effective thermal conductivity (ETC) and radiative characteristics under high temperatures is necessary to improve their performance. This article first analyzes the radiation characteristics of silica aerogels doped with opacifier particles and reinforced fibers, and then a universal model is established to predict the ETC. Furthermore, the impacts of different parameters of opacifier particles and reinforced fibers on the thermal insulation performance of silica aerogels are investigated. The results indicate that SiC exhibits comparatively strong absorption characteristics, making it a good alternative for opacifiers to improve thermal insulation performance under high temperatures. For the given type and volume fraction of opacifier particles, there exists an optimal diameter and volume fraction to achieve the best insulation performance of silica aerogel under a certain temperature. Considering that SiO₂ fibers exhibit a limited extinction capability and higher conductive thermal conductivity under high temperatures, for fiber–particle-reinforced silica aerogels, it is beneficial for their insulation performance to reduce the fiber volume fraction when the required mechanical properties are satisfied. Full article

Duck hepatitis B virus (DHBV) is widely prevalent in global ducks and has been identified in Chinese geese with a high prevalence; the available detection techniques are time-consuming and require sophisticated equipment. In this study, an assay combining multienzyme isothermal rapid amplification (MIRA) and lateral flow dipstick (LFD) was developed for the efficient and rapid detection of DHBV. The primary reaction condition of the MIRA assay for DHBV detection was 10 min at 38 °C without a temperature cycler. Combined with the LFD assay, the complete procedure of the newly developed MIRA assay for DHBV detection required only 15 min, which is about one-fourth of the reaction time for routine polymerase chain reaction assay. And electrophoresis and gel imaging equipment were not required for detection and to read the results. Furthermore, the detection limit of MIRA was 45.6 copies per reaction, which is approximately 10 times lower than that of a routine polymerase chain reaction assay. The primer set and probe had much simpler designs than loop-mediated isothermal amplification, and they were only specific to DHBV, with no cross-reactivity with duck hepatitis A virus subtype 1 and duck hepatitis A virus subtype 3, goose parvovirus, duck enteritis virus, duck circovirus, or Riemerella anatipestifer. In this study, we offer a simple, fast, and accurate assay method to identify DHBV in clinical serum samples of ducks and geese, which would be suitable for widespread application in field clinics. Full article

The aim of this study was to investigate the dynamic changes in the microbial communities of both the environment and gut of Litopenaeus vannamei, as well as to elucidate the mechanisms underlying microbial community assembly in greenhouse farming. 16S rDNA high-throughput sequencing and bioinformatics methods were used to carry out the research on the community structure of the microorganisms under greenhouse culture conditions in water, sediment, and gut microorganisms; correlations pertaining to environmental factors; the feasibility of using Source Tracker; and the mechanisms of community construction. The results show that the dominant microorganisms in water, sediment, and gut farming in a greenhouse environment varied and were subject to dynamic change. A variety of beneficial microbiota such as Bacillus were found in the gut, whereas a variety of microorganisms such as Marivita and Pseudomonas, which function as nitrogen and phosphorus removers, were present in water. Source Tracker and environmental correlation analyses showed that changes in the gut were associated with eutrophication indicators (total nitrogen, total phosphorus, ammonia nitrogen) and changes in environmental microorganisms (in water and sediment). The results of the community-building mechanism analysis show that stochastic processes determine the community-building directions of environmental and gut microorganisms. These findings will help us to understand the microbiota characteristics of shrimp ponds under greenhouse farming conditions, and the complex interactions between the shrimp gut and the environmental microbiota and environmental variables, as well as revealing the changing rules of the gut microbiota. Full article

People with blindness and low vision (pBLV) encounter substantial challenges when it comes to comprehensive scene recognition and precise object identification in unfamiliar environments. Additionally, due to the vision loss, pBLV have difficulty in accessing and identifying potential tripping hazards independently. Previous assistive technologies for the visually impaired often struggle in real-world scenarios due to the need for constant training and lack of robustness, which limits their effectiveness, especially in dynamic and unfamiliar environments, where accurate and efficient perception is crucial. Therefore, we frame our research question in this paper as: How can we assist pBLV in recognizing scenes, identifying objects, and detecting potential tripping hazards in unfamiliar environments, where existing assistive technologies often falter due to their lack of robustness? We hypothesize that by leveraging large pretrained foundation models and prompt engineering, we can create a system that effectively addresses the challenges faced by pBLV in unfamiliar environments. Motivated by the prevalence of large pretrained foundation models, particularly in assistive robotics applications, due to their accurate perception and robust contextual understanding in real-world scenarios induced by extensive pretraining, we present a pioneering approach that leverages foundation models to enhance visual perception for pBLV, offering detailed and comprehensive descriptions of the surrounding environment and providing warnings about potential risks. Specifically, our method begins by leveraging a large-image tagging model (i.e., Recognize Anything Model (RAM)) to identify all common objects present in the captured images. The recognition results and user query are then integrated into a prompt, tailored specifically for pBLV, using prompt engineering. By combining the prompt and input image, a vision-language foundation model (i.e., InstructBLIP) generates detailed and comprehensive descriptions of the environment and identifies potential risks in the environment by analyzing environmental objects and scenic landmarks, relevant to the prompt. We evaluate our approach through experiments conducted on both indoor and outdoor datasets. Our results demonstrate that our method can recognize objects accurately and provide insightful descriptions and analysis of the environment for pBLV. Full article

Candida albicans (Ca), a prominent opportunistic fungal pathogen in humans, has garnered considerable attention due to its infectious properties. Herein, we have identified and characterized CaCDAP1 (Ca orf19.1034), a homolog of ScDAP1 found in Saccharomyces cerevisiae. CaCDAP1 encodes a 183-amino acid protein with a conserved cytochrome b5-like heme-binding domain. The deletion of CaDAP1 renders Ca cells susceptible to caspofungin and terbinafine. CaDAP1 deletion confers resistance to Congo Red and Calcofluor White, and sensitivity to sodium dodecyl sulfate. The deletion of CaDAP1 results in a 50% reduction in chitin content within the cell wall, the downregulation of phosphorylation levels in CaMkc1, and the upregulation of phosphorylation levels in CaCek1. Notably, CaDAP1 deletion results in the abnormal hyphal development of Ca cells and diminishes virulence in a mouse systemic infection model. Thus, CaDAP1 emerges as a critical regulator governing cellular responses to antifungal drugs, the synthesis of cell wall chitin, and virulence in Ca. Full article

The development of a catalyst for the conversion of CO₂ and epoxides to the corresponding cyclic carbonates is still a very attractive topic. Magnetic nano-catalysts are widely used in various organic reactions due to their magnetic separation and recycling properties. Here, a magnetic nano-catalyst containing a Schiff base unit was designed, synthesized and used as a heterogeneous catalyst to catalyze CO₂ and epoxides to form cyclic carbonates without solvents and co-catalysts. The catalyst was characterized using Fourier transform infrared (FTIR), X-ray diffraction (XRD), thermogravimetric (TG), VSM, SEM, TEM and BET. The results show that the magnetic nano-catalyst containing the Schiff base unit has a high activity in the solvent-free cycloaddition reaction of CO₂ with epoxide under mild conditions, and is easily separated from the reaction mixture driven by external magnetic force. The recovered catalyst maintains a high performance after five cycles. Full article

Perfluorinated compounds (PFCs) belong to a significant category of global environmental pollutants. Investigating the toxicological effects of PFCs within biological systems is of critical significance in various disciplines such as life sciences, environmental science, chemistry, and ecotoxicology. In this study, under simulated human physiological conditions (pH = 7.4), a combination of multiple spectroscopic techniques and computational simulations was employed to investigate the impact of perfluorinated compounds (PFCs) on the G protein-coupled estrogen receptor (GPER). Additionally, the research focused on exploring the binding modes and toxicological mechanisms between PFCs and GPER at the molecular level. All three perfluorinated sulfonic acids (PFSAs) can induce quenching of GPER fluorescence through static quenching and non-radiative energy transfer. Steady-state fluorescence calculations at different temperatures revealed apparent binding constants in the order of 10⁶, confirming a strong binding affinity between the three PFSAs and GPER. Molecular docking studies indicated that the binding sites of PFSAs are located within the largest hydrophobic cavity in the head region of GPER, where they can engage in hydrogen bonding and hydrophobic interactions with amino acid residues within the cavity. Fourier transform infrared spectroscopy, three-dimensional fluorescence, and molecular dynamics simulations collectively indicate that proteins become more stable upon binding with small molecules. There is an overall increase in hydrophobicity, and alterations in the secondary structure of the protein are observed. This study deepens the comprehension of the effects of PFCs on the endocrine system, aiding in evaluating their potential impact on human health. It provides a basis for policy-making and environmental management while also offering insights for developing new pollution monitoring methods and drug therapies. Full article

The natural color of biological specimens plays a crucial role in body protection, signaling, physiological adaptations, etc. Full-color optical sectioning structured illumination microscopy (OS-SIM) color is a promising approach that can reconstruct biological specimens in three-dimension meanwhile maintaining their natural color. Full-color OS-SIM takes the advantages of rapid imaging speed, compatibility with fluorescence and non-fluorescence samples, compact configuration, and low cost. However, the commonly used HSV-RMS reconstruction algorithm for full-color OS-SIM faces two issues to be improved. One is the RMS (root-mean-square) OS reconstruction algorithm is prone to background noise, and the other is the reconstruction is bound in RGB and HSV color spaces, consuming more reconstructing time. In this paper, we propose a full-color Fourier-OS-SIM method that allows for the OS reconstruction using the high-frequency spectrum of the sample and thus is immune to the low-frequency background noise. The full-color Fourier-OS-SIM directly runs in the RGB color space, providing an easy way to restore the color information. Simulation and experiments with various samples (pollen grains and tiny animals) demonstrate that the full-color Fourier-OS-SIM method is superior to the HSV-RMS method regarding background noise suppression. Moreover, benefiting from the background noise suppression merit, the quantitative morphological height map analysis with the full-color Fourier-OS-SIM method is more accurate. The proposed full-color Fourier-OS-SIM method is expected to find broad applications in biological and industrial fields where the 3D morphology and the color information of objects both need to be recovered. Full article

This study investigated the bactericidal effects of ultraviolet (UV) radiation, a high-voltage electric field (HVEF), and their combination on Escherichia coli. The results indicated that UV and combined disinfection were more effective with longer exposure, leading to significant reductions in microbial activity. Specifically, the single UV disinfection alone reduced activity by 3.3 log after 5 min, while combined disinfection achieved a 4.2 log reduction. In contrast, short-term HVEF treatment did not exhibit significant bactericidal effects, only achieving a reduction of 0.17 log in 5 min. Furthermore, prolonged exposure to both UV disinfection and an HVEF was found to damage cell membranes, ultimately causing cell death, while shorter durations did not. Despite rapid cell count decreases, flow cytometry did not detect apoptotic or necrotic cells, likely due to rapid cell rupture. This study suggests that combining UV radiation and an HVEF could be a promising approach for inhibiting bacterial reproduction, with HVEF enhancing UV effects. These findings provide insights for using combined HVEF and UV disinfection in food safety and preservation. Full article

In this paper, a 4 amplitude shift keying (4-ASK) fast orthogonal frequency division multiplexing (FOFDM) scheme was experimentally investigated over a turbulent air–water channel for optical wireless communications. The experiment results showed that the 4-ASK-FOFDM modulated signals were not sensitive to weak atmospheric turbulence, and the bit-error rate (BER) was lower than the 7% forward error correction (FEC) limit of 3.8 × 10⁻³. Under the condition of the same spectra efficiency, the 4-ASK-FOFDM scheme just had a tiny performance penalty compared to the 16-QAM-OFDM scheme. Consequently, the 4-ASK-FOFDM scheme is a promising alternative to the conventional 16-QAM-OFDM scheme in optical wireless communications. Full article

Honey is a natural sweetener made by bees that exhibits antimicrobial activity, mainly related to its H₂O₂ content. The aim of this work was to research the H₂O₂ concentration of 24 Spanish honeys from different botanical origins, studying their possible correlation with glucose oxidase (GOx), catalase (CAT), and anti-Staphylococcus aureus activities (minimal inhibition concentration (MIC), minimal bactericidal concentration (MBC), and percentage of inhibition at 5% (w/v) honey against Staphylococcus aureus), as well as possible correlations among all the analyzed parameters. The results showed that the H₂O₂ concentration did not depend on the botanical origin of the honeys. There were neither correlations between the H₂O₂ concentration and the activities of GOx and CAT, nor between GOx and antimicrobial activity. However, CAT and antimicrobial activities were positively correlated. Therefore, CAT could be successfully used as a possible marker of the antimicrobial activity of honeys against Staphylococcus aureus. Furthermore, a linear regression model has been fitted to explain the antimicrobial activity from CAT and GOx activity and H₂O₂ concentration. Although H₂O₂ is one of the compounds involved in honey’s antibacterial activity, this capacity also strongly depends on other honey components (such as low water activity, acidity, osmolarity, and phenolic compounds). The very high anti-Staphylococcus aureus activity exhibited by all samples could be interesting for commercial honey-based formulations also helping to promote local beekeeping. Full article

The evaluation of flow dynamics in fractured shale oil reservoirs presents significant challenges due to the complex pore configurations and high organic material concentration. Conventional methods for petrophysical and fluid dynamic evaluations are insufficient in addressing these complexities. However, nuclear magnetic resonance (NMR) technology is an effective technique for quantitatively delineating fluid micro-transport properties across the reservoir core. This study presents an experimental methodology rooted in NMR technology to quantify the flow capabilities within the shale oil matrix. This approach incorporates high-pressure saturation flow experiments across seven distinct core samples to gauge the micro-transport phenomena of fluids across various pore dimensions. The results revealed that under high-pressure saturation, shale cores devoid of fractures demonstrated an average crude oil saturation rate of merely 19.44%. Cores with evident stratification exhibited a 16.18% increase in flow capacity compared to their non-stratified counterparts. The flow dynamics within these shale reservoirs exhibited a range of behaviors, from non-linear to linear. In lower-permeability zones, non-linear patterns became increasingly apparent. An NMR T2 spectrum analysis was used to identify the minimum effective pore size conducive to shale oil flow within the matrix, which was between 8 and 10 nanometers. These insights provide a foundation for a deeper understanding of the mechanisms behind oil and gas migration in fractured shale oil matrices, offering valuable insight into their extractive potential. Full article

In response to the limitations of existing evaluation methods for gas well types in tight sandstone gas reservoirs, characterized by low indicator dimensions and a reliance on traditional methods with low prediction accuracy, therefore, a novel approach based on a two-dimensional convolutional neural network (2D-CNN) is proposed for predicting gas well types. First, gas well features are hierarchically selected using variance filtering, correlation coefficients, and the XGBoost algorithm. Then, gas well types are determined via spectral clustering, with each gas well labeled accordingly. Finally, the selected features are inputted, and classification labels are outputted into the 2D-CNN, where convolutional layers extract features of gas well indicators, and the pooling layer, which, trained by the backpropagation of CNN, performs secondary dimensionality reduction. A 2D-CNN gas well classification prediction model is constructed, and the softmax function is employed to determine well classifications. This methodology is applied to a specific tight gas reservoir. The study findings indicate the following: (1) Via two rounds of feature selection using the new algorithm, the number of gas well indicator dimensions is reduced from 29 to 15, thereby reducing the computational complexity of the model. (2) Gas wells are categorized into high, medium, and low types, addressing a deep learning multi-class prediction problem. (3) The new method achieves an accuracy of 0.99 and a loss value of 0.03, outperforming BP neural networks, XGBoost, LightGBM, long short-term memory networks (LSTMs), and one-dimensional convolutional neural networks (1D-CNNs). Overall, this innovative approach demonstrates superior efficacy in predicting gas well types, which is particularly valuable for tight sandstone gas reservoirs. Full article

In the process of water displacing oil within mobile pipelines, it is common that the oil tends to accumulate at the elevated sections of inclined pipelines, leading to an issue of residual oil accumulation. In this paper, the mechanism of carrying accumulated oil out of the pipeline with water flow is discussed. Taking the residual oil layer in down-hill pipelines as a research object, a hydrodynamic model of the water-oil displacement process is established based on the theory of liquid–liquid two-phase flow and the application of the momentum transfer equation. It has been found that the use of this model can enhance the computational speed by 15% without affecting the accuracy of the calculations. Subsequently, the model is used to analyze the impact of different initial water-phase velocities, inclination angles, initial oil-phase heights, and pipeline diameters on the oil-carrying process of water flow. The results indicate that increasing the initial water-phase velocity, the angle of inclination, and the initial oil-phase height all enhance the fluctuation in the oil–water interface, making it easier for the oil phase to be carried away from the pipeline. Conversely, when all other parameters are held constant, an increase in the pipeline diameter tends to stabilize the oil–water interface, thereby making it more difficult for the residual oil to be carried away by the water flow. Full article

It is now widely recognized that gut microbiota plays a critical role not only in the development and progression of diseases, but also in its susceptibility to dietary patterns, food composition, and nutritional intake. In this comprehensive review, we have compiled the latest findings on the effects of food nutrients and bioactive compounds on the gut microbiota. The research indicates that certain components, such as unsaturated fatty acids, dietary fiber, and protein have a significant impact on the composition of bile salts and short-chain fatty acids through catabolic processes, thereby influencing the gut microbiota. Additionally, these compounds also have an effect on the ratio of Firmicutes to Bacteroides, as well as the abundance of specific species like Akkermansia muciniphila. The gut microbiota has been found to play a role in altering the absorption and metabolism of nutrients, bioactive compounds, and drugs, adding another layer of complexity to the interaction between food and gut microbiota, which often requires long-term adaptation to yield substantial outcomes. In conclusion, understanding the relationship between food compounds and gut microbiota can offer valuable insights into the potential therapeutic applications of food and dietary interventions in various diseases and health conditions. Full article