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Innovative formulation of microencapsulated “spirubiotic sachets” as a synbiotic treatment for metabolic syndrome patients
Abstract Spirulina platensis (Sp) and probiotic bacteria (Pb) have been shown to have beneficial effects in controlling metabolic syndrome (MeS), characterized by the co-occurrence of obesity, dyslipidemia, hypertension, and type 2 diabetes. The objective of this research was to formulate an innovative microencapsulated powder consisting of a mixture of Sp and Pb, referred to as a “spirubiotic sachet,” and investigate its potential positive impact in managing hyperlipidemia, weight gain, hyperglycemia, and related problems in individuals with metabolic syndrome. Microencapsulated Sp with whey protein, flavored with a coconut taste, was obtained by spray drying, and HPLC analysis was performed. Probiotic bacteria were microencapsulated by freeze-drying, and the characterization of both microcapsules was done. The spirubiotic sachets contained both Sp and Pb. Physico-sensory tests were reported. Fifty diagnosed MeS cases were enrolled in this study. Pre- and post-treatment assessments of clinical, dietary, anthropometric data, and body composition measurements were conducted. Enzymatic colorimetric methods and ELISA kits were used to assess biochemical parameters. Patients received one spirubiotic sachet/day and a tailored diet regimen for 3 months. Administering a spirubiotic sachet to MeS patients effectively reversed the majority of metabolic abnormalities, bringing them closer to normal levels with decreased dietary calories, improved anthropometric measures, altered body composition, and normalized biochemical parameters. In conclusion, Sp and Pb work together synergistically to help manage people with MeS and related diseases. Spirubiotic sachets can be innovative with beneficial health effects.
Research on eco-efficiency of provinces along the Belt and Road in China based on DEA methods and AI prediction algorithms
Estimated glucose disposal rate and frailty in middle and older aged adults from three prospective cohorts
Magnetodynamics of short nanoparticle chains
Abstract In recent years, there has been increasing interest in the understanding and application of nanoparticle assemblies driven by external fields. Although these systems can exhibit marked transitions in behavior compared to non-interacting counterparts, it has often proven challenging to connect their dynamics with underlying physical mechanisms or even to verifiably establish their structure under realistic experimental conditions. We have studied colloidal iron oxide nanoparticles that assemble into ordered, few-particle linear chains under the influence of oscillating and pulsed magnetic fields. In this work, our goal has been to answer the following question: by what physical mechanisms does the magnetic switching of a linear chain evolve from the switching of its constituent particles? Cryo-TEM has been used to flash freeze and image the structures formed by oscillatory drive fields, and magnetic relaxometry has been used to extract the multiple time constants associated with magnetic switching of the short chains. Armed with the physical structure from microscopy and the field-dependent switching times from magnetic measurements, we have conducted extensive micromagnetic simulations, revealing probable physical mechanisms for each time constant regime spanning $$10^{6}$$ ( $$\approx$$ 1 μs to 1 s) in time. These types of magnetic nanomaterials have great potential for biomedical technologies, particularly magnetic particle imaging and hyperthermia, and rigorous elucidation of their physics will hasten their optimization.
Effect of finger millet diet on mental health and meta cognitive skills in young adult athletes and non-athletes
RHO GTPase by L1 GABAergic neurons in frontal cortex and L6 glutamatergic neurons in prefrontal cortex differentiates states of unconsciousness
Real time 3D monitoring of sintering ore temperature enabled by temporal fusion transformers
Abstract Iron ore serves as the fundamental feedstock for blast furnace operations, and its quality is constitutionally affected by the temperature of the mixture during the sintering process. To enhance the early prediction and regulation of the mixture temperature, this study proposes an intelligent 3D prediction model for iron ore temperature based on the Temporal Fusion Transformer (TFT). This model effectively combines advanced multi-horizon forecasting capabilities with temporal dynamic interpretability, while expanding the observation framework into a three-dimensional space through simulation outcomes. Simultaneously, the study focuses on the fluctuation patterns of the major chemical components in sintering materials and their influence on iron ore temperature through the Variational Autoencoder-Temporal Convolutional Networks (VAE-TCN) model. The TFT model, developed using historical sintering data, achieves an $$R^{2} = 0.8572$$ and RMSE = 4.7568 for one-step-ahead prediction of the sinter temperature spatial distribution, based on a dataset split of 90% training, 5% validation, and 5% testing. Compared with Transformer and Long Short-Term Memory (LSTM) networks, the TFT model demonstrates superior performance, reducing RMSE by 0.805 and 2.9937, respectively. In practical applications, the TFT model offers valuable guidance for real-time temperature monitoring during iron ore sintering operations.
Foraging strategy of wood mice for undamaged and moth-infested Castanea crenata nuts on forest floor
Abstract This study investigated the foraging strategy of wood mice for undamaged Castanea crenata nuts and those with exit holes from moth larvae (EH nuts) on the forest floor in 2022 and 2024. Mice were captured using live traps, and nut infestation status was assessed. A behavior experiment was also conducted to observe the mouse responses to nuts. In both years, Apodemus speciosus and A. argenteus were observed. Throughout the entire study period, EH nuts accounted for a large proportion of insect-damaged nuts. Most EH nuts (95/100) showed reduced contents (i.e., mostly cotyledons) (0–40%) and internal discoloration. Nonetheless, almost all nuts were removed overnight, with undamaged nuts removed earlier than EH nuts. Mice sniffed or clutched approximately 50% of the nuts before removal, suggesting olfactory-based evaluation. A higher proportion of undamaged nuts was removed after examination than without examination. These results revealed that although most EH nuts have low feeding value, they remain an important food resource. Mice use olfactory cues to avoid EH nuts and select undamaged nuts; however, the additional evaluation time may increase their risk from predators and competitors.
Meows encode less individual information than purrs and show greater variability in domestic than in wild cats
Abstract Domestication has shaped animal vocal behaviour, increasing flexibility and responsiveness to humans. In domestic cats ( Felis catus ), two vocalisations, meows and purrs, have distinct communicative roles. Meows are context-dependent signals primarily directed at humans; purrs are stereotyped, low-frequency sounds produced in affiliative contexts. Vocal individuality, key in mammalian communication, supports social recognition and interaction, but its presence across cats’ call types remains poorly understood. We examined whether cats encode individual identity in meows and purrs, hypothesising that meows might show stronger signatures due to their human-directed nature. We analysed 276 meows from 14 cats and 557 purrs from 21 cats. Both call types carried sufficient individual information, but purrs had significantly higher classification accuracy (84.6%) and encoded more information content (4.47 bit) than meows (63.2%, 2.65 bit). To place individuality in a domestication framework, we compared domestic cat meows with those of five wild relatives: African wildcat, European wildcat, jungle cat, cheetah, and cougar. Domestic cat meows showed greater acoustic dispersion than those of wild cats, reflecting increased vocal plasticity through domestication. These findings demonstrate how domestication has shaped feline vocalisations, with purrs acting as stable identity cues and meows emphasising flexibility over recognisability.
The relevance of nitrogen-based, high-enthalpy plasmas for effective feedstock treatment in thermal spraying of suspensions
Abstract This work aims to contribute to our understanding of the importance of nitrogen in enhancing feedstock treatment in plasma spraying. Three different plasma gas compositions—a ternary argon-based composition, a ternary nitrogen-based composition, and a nitrogen-based composition without argon—were used with the Axial III™ plasma torch for suspension plasma spraying. The thermodynamic and transport properties of the plasma gas mixtures were calculated and compared. Based on these calculations, the Ability of Acceleration Factors (AAF) and the Ability of Heating Factors (AHF) were determined and correlated with the measured in-flight particle velocities and temperatures. Computational and experimental results indicate that transport properties, specifically thermal conductivity and viscosity, are key factors in thermal feedstock treatment. In this respect, a high nitrogen content is advantageous. Therefore, nitrogen can be considered a high-heat-transfer component of the plasma. Nitrogen also introduces a relatively high enthalpy. Enthalpy describes the amount of energy stored in the plasma gas that can be released. If the energy output is not enough to heat the feedstock adequately, especially when dealing with high feed rates of liquid feedstocks, the enthalpy of the plasma gas can become a limiting factor. In such cases, a high nitrogen content in the plasma gas is advantageous because the higher enthalpy provides more energy than an argon-based plasma. However, this requires a thermally resilient torch concept.
Salt affects structure, function and transcriptome in the giant cells of slime molds
Occurrence of Grapevine fanleaf virus (Nepovirus foliumflabelli) in the Western Black Sea region of Türkiye and phylogenetic analysis based on partial coat protein gene
Optimal interpolation methods for predicting groundwater levels in Abu Dhabi Emirate using ArcGIS
Impact of citrate dialysate on middle molecular weight uremic toxin adsorption and removal in high-flux and MCO dialysis membranes
Implications of exogenous allantoin in enhancing growth and development of Vitis vinifera L. cultivars under semi-arid condition
Gaussian mixture model for enhancing the quality of transmission estimation in optical networks: a machine learning approach
Abstract In recent times, there has been a huge amount of work on utilizing deep learning (DL) to estimate the quality of transmission (QoT) in optical networks. This research depict a lightpath’s quality of transmission to develop advanced fiber-optic communication and networks based on DL technique. We need different primary estimation parameters for advanced optical fiber communication and networks, i.e., modulation formats, baud rate, and code rate.Recently, the QoT for unspecified optical paths relies on various estimation approaches i.e., (1) analytical models estimating physical layer impairments (PLIs) and (2) margined formulas. This paper emphasis on Gaussian mixture model (GMM) based algorithm that can be applied to optimization and sophisticated systems. The model can forecast bit-error rate, and signal-to-noise ratio (SNR) of unknown optical paths with threshold value, traffic volume, and modulation format. The model was trained and tested using features from Korean network topology. The Area Under the ROC Curve (AUC) from the simulated outcome is 1.00, while maintaining the high accuracy, F1 score, low Brier score and low expected calibration error (ECE).
Image-processing pipeline for diagnosing diabetic macular ischaemia in diabetic macular oedema
Abstract Diabetic Macular Ischaemia (DMI) is a critical cause of vision loss, frequently occurring alongside Diabetic Macular Oedema (DMO) in patients with severe diabetic retinopathy. Accurate diagnosis of DMI is essential for assessing the visual prognosis of any intervention. In the context of clinical research for developing novel treatments to improve macular capillary perfusion, traditional imaging techniques often struggle to identify ischaemic areas precisely. Optical Coherence Tomography Angiography (OCTA) offers a non-invasive method for visualising retinal vasculature, providing potential improvements in diagnosis. However, reliable quantification of ischemia remains challenging, particularly in the presence of macular oedema, which can distort OCTA images. This study introduces an image-processing pipeline to evaluate DMI in patients with DMO using OCTA. Using a 3-dimensional methodology, the pipeline quantitatively assesses macular vascular perfusion, accounting for segmentation errors caused by macular oedema. Imaging data from 35 people with DMO and variable degrees of DMI were imaged using three OCTA devices (Heidelberg Spectralis, Optovue Angiovue, and Topcon Triton) and analysed using this 3D methodology. Key metrics including vessel density, skeletonized vessel density, average vessel radius, and our novel metric named Macular Vascular Volume (MVV), were extracted to assess consistency across two-time points, namely pre- and post-treatment with anti-Vascular Endothelial Growth Factor (anti-VEGF) agents and hence before and after resolution of macular oedema. Measurements with the novel process showed stability of the macular vessel metrics pre- and post-oedema resolution, compared with previously described OCTA metrics using current 2D segmentation methods. The proposed pipeline provides robust quantitative tools for evaluating DMI with DMO, with promise in clinical applications for improving diagnostic accuracy and optimising treatment outcomes.
Laser-assisted socket seal surgery using bioactive glass for dental implant site development: a randomized clinical trial
Abstract Preserving the alveolar ridge after extraction is crucial for implants; different biomaterials and techniques allow predictable outcomes. This study aimed to assess laser photo-biomodulation (PBM) as a complement for socket seal surgery (SSS) when extraction sockets are grafted with bioactive glass and sealed by platelet-rich fibrin (PRF) membrane in humans. 45 sites were randomly and equally distributed into 3 groups: Control: PRF membrane for socket seal, with no more interference; it is a unified step in all groups, Test I: bioactive glass, and Test II: received the same as Test I plus PBM-irradiation. Cone-beam computed tomography (CBCT) was used to assess the dimensional changes in height and width measurements of the alveolar ridge at 0 and 9 months postoperatively. Bone samples were obtained from osteotomy sites at the time of implant placement, and then sent for histological staining using hematoxylin and eosin and Masson trichrome stain for assessment of newly formed bone and histomorphometric analysis in terms of percentage (%) of new bone surface area and % of unmineralized bone, respectively. The Control group revealed a significant reduction in alveolar ridge dimensions over time compared to the test groups ( P-value < 0.001). Test II has shown less bone reduction over time, with a P-value < 0.001. Histological results revealed that Test II showed large areas of connected, highly mature bone trabeculae without any collagen fibers or immature matrix in between. Compared to the other two groups, Test II provided superior bone quality. PBM can be considered as a promising adjunctive therapy for socket seal approaches when combined with bioactive glass, as evidenced by the better clinical improvement and the remarkable histological findings for bone repair compared with the non-lased group. Trial registration: The current randomized clinical trial was registered at ClinicalTrials.gov (Registration number: NCT06764732) and it was released on 8/1 /2025 .
Enhancing facility maintenance using knowledge-based BIM systems
Abstract The operation phase of a building’s lifecycle refers to the period during which the building is in active use. So, to keep all these facilities working with acceptable performance, maintenance is necessary to keep the buildings working throughout their life cycle. Building maintenance and facility management are very complicated by traditional methods like personal estimation or manual inspections. On the other hand, the growth of technology, mainly in construction, helps in different fields of construction management. A big example of this technology is Building Information Modeling (BIM), a digital representation tool of buildings at various phases of the construction life cycle. To get more simplification, many commercial plugins for construction maintenance have been issued, which are integrated with the BIM model. Unfortunately, these plugins require specialized knowledge, are complicated, and need expertise, training, and more accuracy. In addition, the needed improvement in the maintenance process to overcome the challenges and obstacles that the organization is currently facing. This paper proposes a decision support system framework plugin to help facility management teams effectively plan, track, and allocate their maintenance budgets with a very simple, flexible, and easy-to-use system. The proposed framework creates maintenance schedules based on the facility’s condition, type of equipment, a frequency of use, and allocates the necessary resources, parts, and labor required for the maintenance phase. It can also generate reports that show resource utilization, expenditure, and other cost-related indicators, making it easier for facility managers to plan their budgets and make informed decisions. The main idea of the proposed framework plugin that was created using Python is to use real-time data and predictive models to optimize maintenance plans, schedules, and resource allocation, facility management teams. The results show reduced downtime, minimized costs, and extended the lifespan of building assets within an actual case study. This framework plugin is an exemplary approach for facilities seeking to implement an innovative yet scalable Facility management (FM) optimum solution.