Scientific Reports最新文献

Dyeing natural fabrics using supercritical carbon dioxide is challenging, especially without essential color hues. This work demonstrated that two newly developed reactive disperse dyes with distinct colors and shades were generated, one of which featured from the anthraquinone family and the other yellow, containing a pyrazole moiety. These new dyes and their combinations were used to dye cotton fabric using supercritical carbon dioxide and the highest K/S values were achieved at 8.73 for the mixture of (blue dye: yellow dye 80:20), however the lowest K/S was observed at 7.71 for (blue dye: yellow dye 20:80). The new dyes' chemical compositions were identified using elemental and spectroscopic analyses. The effectiveness of these dyes and their mixtures for cotton dyeing was discussed. The dyed samples were tested for color fastness, and the results indicated that they had excellent color retention and were highly durable in washing. The increasing patterns in both dyeing rate and build-up curves show good compatibility. Furthermore, desirable shades of green can be achieved by mixing blue and yellow dyes at various ratios in supercritical CO₂. The compatibility test involves calculating color difference index values for dyed cotton fabrics by utilizing various ratios of a binary mixture of dyes. Furthermore, the dyes under study and dyed samples displayed superior antibacterial properties against gram-positive and gram-negative bacteria compared to certain antibiotics used as a control. These results aligned with the quality and eco-friendly standards required by the industry without the use of water.

This study evaluates the safety and potential benefits of PBM on pancreatic beta cells and islets. PBM was applied to insulin-secreting cell lines (MIN6) and rat pancreatic islets using a 670 nm light source, continuous output, with a power density of 2.8 mW/cm², from 5 s to several 24 h. Measure of cell viability, insulin secretion, mitochondrial function, ATP content, and cellular respiration were assessed. Additionally, a diabetic rat model is used for islet transplantation (pre-conditioning with PBM or not) experiments. Short and long-term PBM exposure did not affect beta cell islets viability, insulin secretion nor ATP content. While short-term PBM (2 h) increases superoxide ion content, this was not observed for long exposure (24 h). Mitochondrial respirations were slightly decreased after PBM. In the islet transplantation model, both pre-illuminated and non-illuminated islets improved metabolic control in diabetic rats with a safety profile regarding the post-transplantation period. In summary, for the first time, long-term PBM exhibited safety in terms of cell viability, insulin secretion, energetic profiles in vitro, and post-transplantation period in vivo. Further investigation is warranted to explore PBM's protective effects under conditions of stress, aiding in the development of innovative approaches for cellular therapy.

Age-related macular degeneration (AMD) is the leading cause of blindness in older people in developed countries. It has been suggested that heavy metal exposure may be associated with the development of AMD, but most studies have focused on the effects of a single metal with traditional methods. In this study, we analyzed the relationship between 13 urinary heavy metal concentrations and AMD using NHANES data between 2005 and 2008. We constructed and compared 11 machine learning models to identify the best model for predicting AMD risk. We further interpreted the models by Permutation Feature Importance (PFI), Partial Dependence Plot (PDP) analysis, and SHapley Additive exPlanations (SHAP) analysis. 216 AMD patients out of 2380 participants. The random forest (RF) model performed optimally in predicting the risk of AMD, with an AUC value of 0.970. PFI analyses revealed that age and urinary cadmium (Cd) were the main factors influencing the risk of AMD. SHAP analyses further confirmed the significance of Cd concentration in predicting the risk of AMD, and we revealed a significant interaction with significant interaction of race. Our study firstly explored the relationship between heavy metal exposure levels and AMD based on machine learning techniques, found that urinary Cd concentration had the greatest impact on AMD, and revealed the superior predictive performance of machine learning methods. Furthermore, our study provided a new perspective for early screening and intervention of AMD.

The Irpinia Fault, also known as the Monte Marzano Fault System, located in the Southern Apennines (Italy), is one of the most seismically active structures in the Mediterranean. It is the source of the 1980, Ms 6.9, multi-segment rupture earthquake that caused significant damage and nearly 3,000 casualties. Paleoseismological surveys indicate that this structure has generated at least four Mw ~ 7 surface-rupturing earthquakes in the past 2 ka. This paper presents a comprehensive, high-resolution geophysical investigation focused on the southernmost fault segment of the Monte Marzano Fault System, i.e., the Pantano-Ripa Rossa Fault, outcropping within the Pantano di San Gregorio Magno intramontane basin. The project, named TEst Site IRpinia fAult (TESIRA), was supported by the University of Napoli Federico II to study the near-surface structure of this intra-basin fault splay that repeatedly ruptured co-seismically in the past thousands of years. Our imaging approach included 2D and 3D electrical and seismic surveys, gravimetry, 3D FullWaver electrical tomography, drone-borne GPR and magnetic surveys, and CO₂ soil flux assessment across the surface rupture. This multidisciplinary investigation improved our understanding of the basin shallow structure, providing an image of a rather complex subsurface fault and basin geometry. Seismic data suggest that fault activity at the Pantano segment of MMFS is characterized by a near-surface cumulative displacement greater than previous estimations, calling into question earlier assumptions about the timing of its activation. Despite some challenges with our drone-mounted survey equipment, the integrated dataset provides a comprehensive and reliable image of the subsurface structure. This work demonstrates the utility of developing an integrated approach at high-resolution geophysical imaging and interpretation of fault zones with weak morphological expressions.

This paper presents NN_ILEACH, a novel neural network-based routing protocol designed to enhance the energy efficiency and longevity of Wireless Sensor Networks (WSNs). By integrating the Energy Hole Removing Mechanism (EHORM) with a sophisticated neural network for cluster head selection, NN_ILEACH effectively addresses the energy depletion challenges associated with traditional protocols like LEACH and ILEACH. Our extensive simulations demonstrate that NN_ILEACH significantly outperforms these classical protocols. Specifically, NN_ILEACH extends the network lifetime to an impressive 11,361 rounds, compared to only 505 rounds achieved by LEACH under identical conditions-representing a more than 20-fold improvement. Additionally, NN_ILEACH achieves a 30% increase in throughput and a 25% enhancement in packet delivery ratio, while reducing overall energy consumption by 40%. These results underscore the protocol's potential to optimize energy usage and maintain network stability, paving the way for more resilient IoT systems in dynamic environments. Future work will explore further integration of machine learning techniques to enhance adaptability and performance in WSNs.

Procedural coding presents a taxing challenge for clinicians. However, recent advances in natural language processing offer a promising avenue for developing applications that assist clinicians, thereby alleviating their administrative burdens. This study seeks to create an application capable of predicting procedure codes by analysing clinicians' operative notes, aiming to streamline their workflow and enhance efficiency. We downstreamed an existing and a native German medical BERT model in a secondary use scenario, utilizing already coded surgery notes to model the coding procedure as a multi-label classification task. In comparison to the transformer-based architecture, we were levering the non-contextual model fastText, a convolutional neural network, a support vector machine and logistic regression for a comparative analysis of possible coding performance. About 350,000 notes were used for model adaption. By considering the top five suggested procedure codes from medBERT.de, surgeryBERT.at, fastText, a convolutional neural network, a support vector machine and a logistic regression, the mean average precision achieved was 0.880, 0.867, 0.870, 0.851, 0.870 and 0.805 respectively. Support vector machines performed better for surgery reports with a sequence length greater than 512, achieving a mean average precision of 0.872 in comparison to 0.840 for fastText, 0.837 for medBERT.de and 0.820 for surgeryBERT.at. A prototypical front-end application for coding support was additionally implemented. The problem of predicting procedure codes from a given operative report can be successfully modelled as a multi-label classification task, with a promising performance. Support vector machines as a classical machine learning method outperformed the non-contextual fastText approach. FastText with less demanding hardware resources has reached a similar performance to BERT-based models and has shown to be more suitable for explaining the predictions efficiently.

The dynamic crack propagation trajectories play a crucial role in enhancing our understanding of spatial mechanisms involved in crack expansion. However, visualization of internal cracks under complex crack conditions has always been a challenge. Biological networks have been honed by many cycles of evolutionary selection pressure and are likely to yield reasonable solutions to such combinatorial optimization problems. This study applied the slime mould algorithm to improve the accuracy of internal crack localization in rocks and employed Minimum spanning tree and Gaussian mixture model to construct the crack propagation trajectories. By introducing the concept of bond length, the evolution characteristics of crack levels were effectively characterized. Research results showed that this approach effectively preserves essential crack localization information while mitigating the influence of interfering parameters, providing crack characterization results that exhibit high consistency with actual fracture patterns. The curves of cumulative bond length and relative bond length over time conform to the trend of a Growth/Sigmoidal curve. The strength of the bond was correlated with the temporal process of crack propagation. This result could be helpful for analyzing crack trajectories and predicting rock stability.

This study investigated the chemical properties of peat microparticles modified asphalt (Pt.M.A.). The originality of the study resides in the examination of the chemical characteristics of peat microparticles (Pt.) modified asphalt (Pt. M.A.) utilising FTIR, SEM, SFE, and XRD methodologies. This encompasses Fourier transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM), surface free energy (SFE), and X-ray diffraction (XRD). Initially, FTIR examined the functional groups of both unaltered and altered asphalt binders. The SEM images reveal improved compatibility, showcasing superior diffusion of the modifier across the asphalt. A further critical factor is that improved adhesion properties, according to the SFE study, indicate that modified binders generally offer more SFE compared to unmodified binders. The XRD measurements revealed a semi-crystalline structure in the Pt. modifier and an amorphous structure in the basal asphalt binder. The integration of Pt. into the asphalt cement resulted in modifications to the phases of both constituents, culminating in the emergence of a new semi-crystalline phase inside the modified asphalt binder. These data suggest that peat microparticles (Pt.) can improve the efficacy of asphalt binders by enhancing compatibility, adhesion, and resistance to ageing.

Objective: To investigate the association between coal dust exposure and the occurrence of dyslipidemia in coal mine workers, and identify relevant risk factors. Methods: We selected a population who underwent occupational health examinations at Huainan Yangguang Xinkang Hospital from March 2020 to July 2022. Participants were divided into two groups based on the presence or absence of dyslipidemia, and their baseline information was collected, including records of coal dust exposure. We employed single-factor analysis to identify risk factors for dyslipidemia and adjusted for confounding factors in the adjusted models. Additionally, we explored the effects in different populations using stratified analysis, smooth curve fitting, and propensity score matching. Finally, we confirmed the causal relationship between coal dust exposure and dyslipidemia by examining tissue sections and lipid-related indicators in a mouse model of coal dust exposure. Results A total of 5,657 workers were included in the study, among whom 924 individuals had dyslipidemia and 4,743 individuals did not have dyslipidemia. The results of the single-factor analysis revealed that dust exposure, age, BMI, blood pressure, and smoking were statistically significant risk factors for dyslipidemia (p < 0.05). Additionally, the three multivariate models, adjusted for different confounders, consistently showed a significant increase in the risk of dyslipidemia associated with coal dust exposure (Model 1: OR, 1.869; Model 2: OR, 1.863; Model 3: OR, 2.033). After conducting stratified analysis, this positive correlation remained significant. Furthermore, propensity score matching analysis revealed that with increasing years of work, the risk of dyslipidemia gradually increased, reaching 50% at 11 years. In the mouse model of coal dust exposure, significant coal dust deposition was observed in the lungs and livers of the mice, accompanied by elevated levels of total cholesterol (TC), alanine transaminase (ALT), aspartate transaminase (AST), and low-density lipoprotein cholesterol (LDL-C). Conclusion Exposure to coal dust significantly increases the risk of developing dyslipidemia, and this positive correlation exists in different populations, particularly with increasing years of work, resulting in a higher risk.

Prior literature has debated whether syntax is separable from semantics in the brain. Using functional magnetic resonance imaging and multi-voxel pattern analysis, our previous studies investigated brain activity during morpho-syntactic versus semantic processing. These studies only detected semantic specialization in activation patterns and no syntactic specialization in 5- to 6-year-old and 7- to 8-year-old children. To examine if older children who have mastered morpho-syntactic skills would show specialization for syntax, the current study examined 64 9- to 10-year-old children using the same design and analyses. We observed that only the left IFG pars opercularis was sensitive to syntactic but not semantic information, supporting the hypothesis that this region serves as a core region for syntax. In addition, the left STG which has been implicated in the integration of semantics and syntax, as well as the left MTG and IFG pars triangularis which have been implicated in semantics, were sensitive to both semantic and syntactic information with no evidence of specialization. These findings suggest a lexicalized view of syntax, which argues that semantically sensitive regions are also critical regions for syntactic processing during language comprehension.