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Background: The objective of this study was to extract maternal plasma D-dimer test results from a large medical records dataset and to establish a reference level for pregnant women in our center. This study aimed to investigate the role and efficacy of D-dimer levels for predicting complications in high-risk pregnant women. Methods: The clinical data of 19,117 women who delivered at Wenzhou People’s Hospital between January 1, 2020, and September 30, 2022, were retrospectively analyzed. A total of 74,401 D-dimer test results were extracted from the laboratory information system. Following data screening and matching, 7692 records of D-dimer test results for healthy pregnant women and 19,005 records for pregnant women with complications were selected. D-dimer reference values were calculated for early pregnancy (1–13 weeks gestation), mid-pregnancy (14–27 weeks gestation), late pregnancy (28–36 weeks gestation), and the end of pregnancy (37–41 weeks gestation). A logistic regression model was used to plot receiver operating characteristic (ROC) and determine cut-off values for predicting pregnancy complications in each phase. Results: The mean D-dimer levels in the healthy pregnancy group were 0.56 mg/L in early pregnancy, 1.08 mg/L in mid-pregnancy, 1.48 mg/L in late pregnancy, and 1.89 mg/L at the end of pregnancy. D-dimer levels were greater in the complication group (which contains 11 common pregnancy disorders, including premature rupture of membranes (PRM), gestational diabetes mellitus (GDM), gestational hypertension (GHP), preeclampsia (PE), placental abnormalities, thyroid dysfunction, hemorrhage, thrombophilia, fetal macrosomia, preterm delivery, and infections) than in the healthy group in all pregnancy phases, with statistically significant differences observed in early, medium, and the end of pregnancy. D-dimer levels showed good diagnostic performance in predicting pregnancy complications in early pregnancy (area under the curve (AUC) = 0.71, cut-off value = 0.87, odds ratio: 7.12, 95% confidence interval (CI): 5.41–9.36, p-value = 0.000). Conclusions: The reference range of D-dimer increases with the progression of gestational phases and demonstrates predictive efficacy for high-risk pregnancy complications during early pregnancy.

Target strength (TS) and circular synthetic aperture sonar (CSAS) images provide essential information for active acoustic detection and recognition of non-cooperative unmanned undersea vehicles (UUVs), which pose a significant threat to underwater preset facilities. To access them, we propose an iterative physical acoustics (IPA)-based method to simulate the multiple acoustic scattered fields on rigid surfaces in high-frequency cases. It uses the Helmholtz integral equation with an appropriate Green's function in terms of the Neumann series, and then incorporates the ideas of triangulation and iteration into a numerical implementation. Then two approximate analytic formulae with precise physical meanings are derived to predict the TS and CSAS images of concave targets, respectively. There are no restrictions on the surface's curvature and the order of multiple scattering. The method is validated against the finite element method (FEM) for acoustic scattering from a sphere segment and against an experiment involving an X-rudder UUV's stern. On this basis, we simulate and analyze the TS and CSAS images of an X-rudder UUV. In addition, the influence of the angle of adjacent rudders on the multiple scattering characteristics is discussed. Results show that this method can potentially predict accurate UUV features, especially the multiple scattered features.

Background: Postpartum haemorrhage (PPH) is a serious complication and a cause of maternal mortality after delivery. This study used machine learning algorithms and new feature selection methods to build an efficient PPH risk prediction model and provided new ideas and reference methods for PPH risk management. Methods: The clinical data of women who gave birth at Wenzhou People’s Hospital from 1 January 2021, to 30 March 2022, were retrospectively analysed, and the women were divided into a high haemorrhage group (337 patients) and a low haemorrhage group (431 patients) based on the amount of blood loss. Machine learning algorithms were used to identify the features associated with postpartum haemorrhage from multiple clinical variables using feature selection methods, such as recursive feature elimination (RFE), recursive feature elimination with cross-validation (RFECV), and SelectKBest, and to establish prediction models. Results: For all women, the features associated with postpartum haemorrhage were ‘age’, ‘newborn weight’, ‘gestational week’, ‘perineal laceration’, and ‘caesarean section’. The prediction model established by the random forest classifier performed best, with an F1 score of 0.73 and an area under the curve (AUC) of 0.84. For women who underwent caesarean section or had a vaginal delivery, the features associated with postpartum haemorrhage risk were different. The risk factors for postpartum haemorrhage in women who underwent caesarean section were ‘age’, ‘parity’, ‘preterm birth’, and ‘placenta previa’. The prediction model established by the random forest classifier performed best, with an F1 value of 0.96 and an AUC of 0.95. The risk factors for postpartum haemorrhage in women with vaginal delivery were ‘age’, ‘parity’, ‘gestational week’, ‘diabetes’, ‘assisted reproduction’, ‘hypertension (preeclampsia)’, and ‘multiple pregnancy’. The prediction model established by the AdaBoost classifier performed best, with an F1 value of 0.65 and an AUC of 0.76. Conclusions: Machine learning algorithms can effectively identify the features associated with postpartum haemorrhage risk from clinical variables and establish accurate prediction models, offering a novel approach for clinicians to assess the risk of and prevent postpartum haemorrhage.

ABSTRACT Age is an important factor that determines the physiological functions of the human body, but the changes in maternal physiology, biochemistry, and intestinal flora related to reproductive age and their impact on offspring are not clear. Here, we tested and analyzed the clinical physiological and biochemical indicators and/or intestinal flora, matching the data of 252 parturients and their newborns. We found that 4 clinical indicators, including the white blood cell count and the absolute value of monocytes, were significantly related to the reproductive age (P < 0.05). The composition of the intestinal flora also varied with age, and the intestinal flora of advanced-age women (≥35 years old) was different from that of middle-aged women (>25 and

In order to improve the edge performance of the noisy image, based on the analysis of the image edge noise, the artificial neural network was used to respond and identify the noise, and then all the noise data were obtained, and the noise was filtered. Firstly, according to the noise coefficient characteristics of the image, the image edge information was divided into various types. Secondly, the edge detector was used as the neuron of noise feature, and the antibody was used as neural feedback in the artificial neural network, image edge information was fused and output. Finally, the detection of image edge information is realized. The results show that compared with the traditional algorithm, the edge performance of noisy image is improved, and the detection time is shortened in the case of more edge points.

Introducing negative items into sentences can shift the polarity of emotional words and leads to misclassification. Therefore, dealing with the negative item is indispensable to the analysis of the polarity of tweets. This paper first uses the combination of Conjunction Analysis (CA) technology and Punctuation Mark Identification (PMI) technology to detect negation cue and its scope. Besides, we propose the OL-DAWE model, which uses Data Augmentation(DA) technology to generate opposed tweets according to the original tweet. The model extends the training data set, and test data set and learns the original and opposed sides of the tweet in the training module. When predicting the polarity of tweets, the OL-DAWE model considers the positive degree (negative degree) of the original tweet and the negative degree (positive degree) of its opposed tweet. We conduct experiments on two real-world data sets. We prove the effectiveness of our combined technology in negation processing and show that the OL-DAWE model in the polarity sentiment analysis of tweets is better than the baseline for its simplicity and high efficiency.

In order to accurately evaluate the health status of relay protection equipment and improve the management level of relay protection equipment, a relay protection status evaluation method based on multi-source information fusion technology was proposed, in which the technical characteristics of relay protection was fully considered. The evaluation system and index of relay protection were established. The key technical problems of the multi-source information fusion technology used in the state evaluation of relay protection, the function and structure model of state evaluation of relay protection and its application in the life cycle equipment management were solved. The state evaluation method of relay protection was established. Combining with the current situation of life cycle management of relay protection equipment, a solution to the application of relay protection status evaluation results in the process of equipment life cycle management was put forward. A relay protection status maintenance system based on multi-source information fusion technology was developed, being applied to many dispatching units. The correctness and practicability of this method was verifiesd.

Resources and environment management have always been a research hotspot. In the context of sustainable development and environmental governance, scholars and policy makers have been increasing their research efforts on natural resource utilization and its environmental impact. By using the Web of Science Core Collection database, this article applies the bibliometric method to accomplish a systematic review about studies on accounting and management of natural resource consumption based on input-output method. The results indicate that both in terms of the quantity and quality of academic achievements and international cooperation, China is in high academic position and has made great contributions to the development in this research field. While energy and water account for a large proportion of the study objects, more attention is paid on the other kinds of natural resources, such as land, metal, and ocean. International trade is an eternal hot topic in this field. With the continuous progress of the multi-regional input-output model, the importance and feasibility in the analysis of sub-national level or region in the global supply chain gradually emerged. Combining input-output model with other methods can obtain more comprehensive and accurate results for scientific decision-making. Meanwhile, the uncertainty and limitations inherent in such models clearly need further attention.

Giant axonal neuropathy (GAN) is a very rare autosomal recessive disorder characterized by abnormally large and dysfunctional neuronal axons. Mutations in the GAN gene have been identified as the cause of this disorder. In this report, we performed a detailed phenotypic assessment of a Chinese patient with GAN. An array-based exon capture test and targeted next-generation sequencing were used to detect the suspected mutation sites. Compound heterozygous mutations of p.S79L (c.236C > T) in the BTB domain and p.T489S (c.1466C > G) in the kelch domain were identified in the proband’s genome. S79L was a known mutation, and T489S was reported for the first time. The p.S79L and p.T489S were confirmed in the proband’s mother and father, respectively. Both mutations were located in highly conserved regions and affected the predicted protein crystal structures. The proband’s sural biopsy revealed the classical GAN phenotype of swollen axons filled with closely packed neurofilaments. The combined application of the next-generation sequencing platform and bioinformatics analyses was an effective method for diagnosing GAN. The novel compound mutations of S79L and T489S in the GAN gene were likely the cause of the patient’s GAN symptoms. Our findings enrich the spectrum of mutations associated with this rare type of axonopathy.

It is well known that different coral species have different tolerances to thermal or cold stress, which is presumed to be related to the density of Symbiodinium. However, the intrinsic factors between stress-tolerant characteristics and coral-associated bacteria are rarely studied. In this study, 16 massive coral and 9 branching coral colonies from 6 families, 10 genera, and 18 species were collected at the same time and location (Xinyi Reef) in the South China Sea to investigate the bacterial communities. The results of an alpha diversity analysis showed that bacterial diversities associated with massive corals were generally higher than those with branching corals at different taxonomic levels (phylum, class, order, and so on). In addition, hierarchical clustering tree and PCoA analyses showed that coral species were clustered into two large groups according to the similarity of bacterial communities. Group I consisted of massive Goniastrea, Plesiastrea, Leptastrea, Platygyra, Echinopora, Porites, and Leptoria, and group II consisted of branching Acropora and Pocillopora. These findings suggested that both massive corals and branching corals have their own preference for the choice of associated bacteria, which may be involved in observed differences in thermal/cold tolerances. Further analysis found that 55 bacterial phyla, including 43 formally described phyla and 12 candidate phyla, were detected in these coral species. Among them, 52 phyla were recovered from the massive coral group, and 46 phyla were recovered from the branching coral group. Formally described coral pathogens have not been detected in these coral species, suggesting that they are less likely to be threatened by disease in this geographic area. This study highlights a clear relationship between the high complexity of bacterial community associated with coral, skeletal morphology of coral and potentially tolerances to thermal or cold stress.

Shallow water depth measurements using multispectral images are crucial for marine surveying and mapping. At present, relevant studies either depend on the use of other auxiliary data (such as field water depths or water column data) or contain too many unknown variables, thus making these studies suitable only for images that contain enough visible wavebands. To solve this problem, a Quasi-Analytical Algorithm (QAA) approach is proposed in this paper for estimating the water depths around Weizhou Island by developing a QAA to estimate the diffuse attenuation coefficients and simplifying the parameterization of the bathymetric model. The approach contains an initialization sub-approach and a novel global adjustment sub-approach. It is not only independent of other auxiliary data but also greatly reduces the number of unknowns. Experimental results finally demonstrated that the Root Mean Square Errors (RMSEs) were 1.01 m and 0.77 m for the ZY-3 image and the WorldView-3 (WV-3) image, respectively, so the approach is competitive to other QAA bathymetric methods. Besides, the global adjustment sub-approach was also seen to be superior to common smoothing filters: if the Signal-to-Noise Ratio (SNR) is as low as 42, i.e., ZY-3, it can smooth the water depths and improve the accuracies, otherwise can avoid the over-smoothing of water depths.

Target strength (TS) and circular synthetic aperture sonar (CSAS) images provide essential information for active acoustic detection and recognition of non-cooperative unmanned undersea vehicles (UUVs), which pose a significant threat to underwater preset facilities. To access them, we propose an iterative physical acoustics (IPA)-based method to simulate the multiple acoustic scattered fields on rigid surfaces in high-frequency cases. It uses the Helmholtz integral equation with an appropriate Green's function in terms of the Neumann series, and then incorporates the ideas of triangulation and iteration into a numerical implementation. Then two approximate analytic formulae with precise physical meanings are derived to predict the TS and CSAS images of concave targets, respectively. There are no restrictions on the surface's curvature and the order of multiple scattering. The method is validated against the finite element method (FEM) for acoustic scattering from a sphere segment and against an experiment involving an X-rudder UUV's stern. On this basis, we simulate and analyze the TS and CSAS images of an X-rudder UUV. In addition, the influence of the angle of adjacent rudders on the multiple scattering characteristics is discussed. Results show that this method can potentially predict accurate UUV features, especially the multiple scattered features. [ABSTRACT FROM AUTHOR]