Scientific Reports最新文献

Referring to the intestinal ischemic injury caused by sudden interruption of the blood supply, acute mesenteric ischemia (AMI) is a highly fatal emergency with mortality rates varying from 58 to 80%. The aim of this study was to explore the effect of temperature on AMI admission. This was a retrospective, multicentric study. The medical records of 1477 patients with verified AMI who were consecutively admitted to 3 hospitals anytime between January 2010 and December 2020 were included in the study. Distributed lag non-linear model was applied, the model was adjusted for temperature, atmospheric pressure, relative humidity, year, holiday, day of the week, time and seasonality. AMI exhibited obvious sex preference, AMI patients tended to be male (M/F ratio = 2.3:1) and in their late 50 s. Hospital admissions of acute mesenteric arterial thromboembolism (AMAT) increased significantly with high temperatures on day of exposure and lag 0–14 day. The effect curve of daily average temperature on acute mesenteric venous thromboembolism (AMVT) admission was J-shaped, and the duration of cold effect was longer, while the duration of heat effect was shorter. An increase in hospital admissions of AMVT was found above 20 °C at lag 0–30. For the first time, our study indicated that temperature is significantly associated with the risk of AMI. Although it is not possible to always avoid exposure to extreme temperatures, one should be aware of dramatic temperature fluctuations and take appropriate precautions.

Oxidative stress in adipose tissue may alter the secretion pattern of adipocytokines and potentially promote atherosclerosis. However, the therapeutic role of hydrogen in adipose tissue under oxidative stress remains unclear. In this study, subcutaneous adipose tissue (SCAT) was collected from the mid-thoracic wounds of 12 patients who underwent open-heart surgery with a mid-thoracic incision. The adipose tissue was then immersed in a culture medium dissolved with hydrogen, which was generated using a hydrogen-generating device. The weight of the adipose tissue was measured before and after hydrogenation, and the tissue was immunostained for nuclear factor erythroid 2-related factor 2 (Nrf2), heme oxygenase-1 (HO-1), and superoxide dismutase (SOD), which are markers of oxidative stress. The immunostaining results showed that HO-1 and Nrf2 expression levels were significantly decreased in the hydrogenated group, whereas SOD expression levels increased, but did not attain statistical significance. Image analysis of adipose tissue revealed that a reduction in adipocyte size. Furthermore, hydrogenated adipose tissue showed a trend toward increased gene expression levels of adiponectin and decreased gene expression levels of chemerin, an adipocytokine involved in adipogenesis. These results demonstrated the therapeutic potential of hydrogen gas for oxidative stress in adipose tissue and for reducing adipocyte size.

Although the critical path method (CPM) is effective for the integrated scheduling of small-batch orders, its overemphasis on vertical process relationships and neglect of horizontal parallel relationships have imposed limitations on scheduling, often leading to suboptimal outcomes in terms of the total product completion time. This study introduces an innovative algorithm designed to overcome these limitations and further optimize the total processing time of products. We propose a strategy of "exchanging adjacent processes on the same device", which operates based on the scheduling results of the CPM. By swapping adjacent and interchangeable processes within the constraints of the problem, this algorithm generates multiple new scheduling schemes, effectively expanding the solution space. This expansion enables the discovery of optimized solutions that leverage "horizontal parallel relationships", which is crucial for reducing the "total processing time of products". Finally, the effectiveness of the proposed algorithm is verified through experiments.

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease characterized by motor neuron degeneration. Dysregulation of long non-coding RNAs (lncRNAs) has been implicated in ALS pathogenesis but their roles remain unclear. Previous studies found lnc-ABCA12-3 was downregulated in ALS patients. We aim to characterize the expression and function of lnc-ABCA12-3 in ALS and explore its mechanisms of action. Lnc-ABCA12-3 expression was analyzed in PBMCs from ALS patients and correlated with clinical outcomes. Effect of modulating lnc-ABCA12-3 expression was assessed in cell models using assays of apoptosis, protein homeostasis and pathway analysis. RNA pull-down and interaction studies were performed to identify lnc-ABCA12-3 binding partners. Lnc-ABCA12-3 was downregulated in ALS patients, correlating with faster progression and shorter survival. Overexpression of lnc-ABAC12-3 conferred protection against oxidative stress-induced apoptosis, while knockdown lnc-ABCA12-3 enhanced cell death. Lnc-ABCA12-3 maintained protein quality control pathways, including ubiquitination, autophagy and stress granule formation, by regulating the ubiquitin shuttle protein UBQLN1. This study identified lnc-ABCA12-3 as a novel regulatory lncRNA implicated in ALS pathogenesis by modulating cellular survival and stress responses through interactions with UBQLN1, influencing disease progression. Lnc-ABCA12-3 may influence ALS through regulating protein homeostasis pathways.

Accurately detecting voltage faults is essential for ensuring the safe and stable operation of energy storage power station systems. To swiftly identify operational faults in energy storage batteries, this study introduces a voltage anomaly prediction method based on a Bayesian optimized (BO)-Informer neural network. Firstly, the temporal characteristics and actual data collected by the battery management system (BMS) are considered to establish a long-term operational dataset for the energy storage station. The Pearson correlation coefficient (PCC) is used to quantify the correlations between these data. Secondly, an Informer neural network with BO hyperparameters is used to build the voltage prediction model. The performance of the proposed model is assessed by comparing it with several state-of-the-art models. With a 1 min sampling interval and one-step prediction, trained on 70% of the available data, the proposed model reduces the root mean square error (RMSE), mean square error (MSE), and mean absolute error (MAE) of the predictions to 9.18 mV, 0.0831 mV, and 6.708 mV, respectively. Furthermore, the influence of different sampling intervals and training set ratios on prediction results is analyzed using actual grid operation data, leading to a dataset that balances efficiency and accuracy. The proposed BO-based method achieves more precise voltage abnormity prediction than the existing methods.

A large-scale, high-speed, long-runout landslide occurred in Xinmo Village, Maoxian, Sichuan Province, China, on June 24, 2017. It was characterized by fast sliding speed, rapid volume growth, and large impact area. The dynamic process of such landslides and the influence of erosion on the dynamic process are studied by field investigation, numerical inversion and simulation. The results showed that entrainment erosion was a major factor of landslide volumetric change and a salient feature of the landslide process. An exponential equation relating the erosion rate and the deposition volume was established. Moreover, the study found that a slight change of the erosion rate (1e-4) also had a significant impact on the lateral spreading, longitudinal runout, and vertical erosion. As the erosion rate increased, the lateral spreading, longitudinal runout, and vertical erosion of this type of landslide became more obvious. By using the coefficient of variation method, it was obtained that the erosion rate had a greater influence on the vertical erosion than on the lateral spreading and longitudinal runout. In the study of the velocity of the rock avalanche under different erosion conditions, it was found that the erosion amount and the landslide velocity were not strictly linearly related. This study has important significance for understanding the dynamic process and erosion effect of rock avalanche, and provides useful references and insights for future research in related fields.

Gestational diabetes mellitus (GDM) adversely affects offspring glucose homeostasis and risk of developing obesity. Here, we examined the association between glycemia in pregnant women with overweight or obesity without GDM and offspring metabolic health. Maternal fasting glucose concentrations and glucose 2-h after an oral glucose tolerance test (OGTT) were measured in 208 women with a pre-pregnancy body mass index (BMI) of 28–45 kg/m² without GDM. Offspring outcomes were collected at birth, 3, and 5 years of age. Linear mixed models with time as fixed factor and subject ID as random effects were used for analysis. No associations were found between maternal fasting or 2-h glucose concentrations with offspring glucose and insulin concentrations from birth to 5 years of age. However, maternal fasting glucose in GW 28 and 36, and 2-h OGTT glucose in GW 28 were positively associated with C-peptide concentration at birth. Maternal fasting glucose concentrations in GW 28 and 36 were positively associated with weight-for-length, and maternal fasting glucose in GW 36 was associated with BMI z-score at birth. In summary, blood glucose in pregnant women with overweight or obesity is positively associated with offspring C-peptide concentration, weight-for-length, and BMI z-score at birth, even in the absence of GDM.

Phylogenomic data are revolutionizing the field of insect phylogenetics. One of the most tenable and cost-effective methods of generating phylogenomic data is target enrichment, which has resulted in novel phylogenetic hypotheses and revealed new insights into insect evolution. Orthoptera is the most diverse insect order within polyneoptera and includes many evolutionarily and ecologically interesting species. Still, the order as a whole has lagged behind other major insect orders in terms of transitioning to phylogenomics. In this study, we developed an Orthoptera-specific target enrichment (OR-TE) probe set from 80 transcriptomes across Orthoptera. The probe set targets 1828 loci from genes exhibiting a wide range of evolutionary rates. The utility of this new probe set was validated by generating phylogenomic data from 36 orthopteran species that had not previously been subjected to phylogenomic studies. The OR-TE probe set captured an average of 1037 loci across the tested taxa, resolving relationships across broad phylogenetic scales. Our detailed documentation of the probe design and bioinformatics process is intended to facilitate the widespread adoption of this tool.

This paper presents a comprehensive comparison between Vision Transformers and Convolutional Neural Networks for face recognition related tasks, including extensive experiments on the tasks of face identification and verification. Our study focuses on six state-of-the-art models: EfficientNet, Inception, MobileNet, ResNet, VGG, and Vision Transformers. Our evaluation of these models is based on five diverse datasets: Labeled Faces in the Wild, Real World Occluded Faces, Surveillance Cameras Face, UPM-GTI-Face, and VGG Face 2. These datasets present unique challenges regarding people diversity, distance from the camera, and face occlusions such as those produced by masks and glasses. Our contribution to the field includes a deep analysis of the experimental results, including a thorough examination of the training and evaluation process, as well as the software and hardware configurations used. Our results show that Vision Transformers outperform Convolutional Neural Networks in terms of accuracy and robustness against distance and occlusions for face recognition related tasks, while also presenting a smaller memory footprint and an impressive inference speed, rivaling even the fastest Convolutional Neural Networks. In conclusion, our study provides valuable insights into the performance of Vision Transformers for face recognition related tasks and highlights the potential of these models as a more efficient solution than Convolutional Neural Networks.

Salinity stress negatively affects the growth and yield of crops worldwide. Onion (Allium cepa L.) is moderately sensitive to salinity. Beneficial microorganisms can potentially confer salinity tolerance. This study investigated the effects of endomycorrhizal fungi (M), Pseudomonas putida (Ps) and their combination (MPs) on onion growth under control (0 ppm), moderate (2000 ppm) and high (4000 ppm) NaCl salinity levels. A pot experiment was conducted with sandy loam soil and onion cultivar Giza 20. Results showed that salinity reduced growth attributes, leaf pigments, biomass and bulb yield while increasing oxidative stress markers. However, individual or combined inoculations significantly increased plant height, bulb diameter and biomass production compared to uninoculated plants under saline conditions. MPs treatment provided the highest stimulation, followed by Pseudomonas and mycorrhizae alone. Overall, dual microbial inoculation showed synergistic interaction, conferring maximum benefits for onion growth, bulbing through integrated physiological and biochemical processes under salinity. Bulb yield showed 3.5, 36 and 83% increase over control at 0, 2000 and 4000 ppm salinity, respectively. In conclusion, combined application of mycorrhizal-Pseudomonas inoculations (MPs) effectively mitigate salinity stress. This approach serves as a promising biotechnology for ensuring sustainable onion productivity under saline conditions.