Prenatal diagnosis of double aortic arch with subaortic left brachiocephalic vein and right-side ductus arteriosus using high-definition flow render mode and spatiotemporal image correlation.

Double aortic arch (DAA) with subaortic left brachiocephalic vein (LBCV) and right-side ductus arteriosus (RDA) was not reported before delivery, only in adults with anatomy course findings. We present a case of fetal DAA with subaortic LBCV and RDA using high-definition (HD) flow render mode and spatiotemporal image correlation (STIC).

An unusual case of prenatal diagnosis of isolated subaortic left brachiocephalic vein with HDlive flow and spatiotemporal image correlation(STIC).

Subaortic left brachiocephalic vein (LBCV) is rare in prenatal diagnosis. Reported herein is a case of subaortic a LBCV diagnosed using the HDlive flow and spatiotemporal image correlation with postnatal outcomes evaluation in our hospital.

[Genetic analysis of two Chinese families with maple syrup urine disease].

OBJECTIVE: To carry out genetic analysis for 3 children from two Chinese families affected with maple syrup urine disease (MSUD). METHODS: Target capture - next-generation sequencing and Sanger sequencing were used to detect pathogenic variants associated with MSUD. RESULTS: The proband from family 1 was found to harbor homozygous c.560G>T (p.Gly187Val) variant of the BCKDHB gene (NM_000056), whilst the two patients from family 2 were found to harbor compound heterozygous variants c.197-2A>G (splicing)/c.218delT (p.F74Sfs*4) of the BCKDHB gene. Among these, the c.560G>T and c.218delT variants were unreported previously. CONCLUSION: The new variants discovered in this study have expanded the mutational spectrum of the BCKDHB gene.

Evolution and emerging trends of named entity recognition: Bibliometric analysis from 2000 to 2023.

Identifying valuable information within the extensive texts documented in natural language presents a significant challenge in various disciplines. Named Entity Recognition (NER), as one of the critical technologies in text data processing and mining, has become a current research hotspot. To accurately and objectively review the progress in NER, this paper employs bibliometric methods. It analyzes 1300 documents related to NER obtained from the Web of Science database using CiteSpace software. Firstly, statistical analysis is performed on the literature and journals that were obtained to explore the distribution characteristics of the literature. Secondly, the core authors in the field of NER, the development of the technology in different countries, and the leading institutions are explored by analyzing the number of publications and the cooperation network graph. Finally, explore the research frontiers, development tracks, research hotspots, and other information in this field from a scientific point of view, and further discuss the five research frontiers and seven research hotspots in depth. This paper explores the progress of NER research from both macro and micro perspectives. It aims to assist researchers in quickly grasping relevant information and offers constructive ideas and suggestions to promote the development of NER.

Reptile Search Algorithm Considering Different Flight Heights to Solve Engineering Optimization Design Problems.

The reptile search algorithm is an effective optimization method based on the natural laws of the biological world. By restoring and simulating the hunting process of reptiles, good optimization results can be achieved. However, due to the limitations of natural laws, it is easy to fall into local optima during the exploration phase. Inspired by the different search fields of biological organisms with varying flight heights, this paper proposes a reptile search algorithm considering different flight heights. In the exploration phase, introducing the different flight altitude abilities of two animals, the northern goshawk and the African vulture, enables reptiles to have better search horizons, improve their global search ability, and reduce the probability of falling into local optima during the exploration phase. A novel dynamic factor (DF) is proposed in the exploitation phase to improve the algorithm's convergence speed and optimization accuracy. To verify the effectiveness of the proposed algorithm, the test results were compared with ten state-of-the-art (SOTA) algorithms on thirty-three famous test functions. The experimental results show that the proposed algorithm has good performance. In addition, the proposed algorithm and ten SOTA algorithms were applied to three micromachine practical engineering problems, and the experimental results show that the proposed algorithm has good problem-solving ability.

Multi-Strategy Improved Sand Cat Swarm Optimization: Global Optimization and Feature Selection.

The sand cat is a creature suitable for living in the desert. Sand cat swarm optimization (SCSO) is a biomimetic swarm intelligence algorithm, which inspired by the lifestyle of the sand cat. Although the SCSO has achieved good optimization results, it still has drawbacks, such as being prone to falling into local optima, low search efficiency, and limited optimization accuracy due to limitations in some innate biological conditions. To address the corresponding shortcomings, this paper proposes three improved strategies: a novel opposition-based learning strategy, a novel exploration mechanism, and a biological elimination update mechanism. Based on the original SCSO, a multi-strategy improved sand cat swarm optimization (MSCSO) is proposed. To verify the effectiveness of the proposed algorithm, the MSCSO algorithm is applied to two types of problems: global optimization and feature selection. The global optimization includes twenty non-fixed dimensional functions (Dim = 30, 100, and 500) and ten fixed dimensional functions, while feature selection comprises 24 datasets. By analyzing and comparing the mathematical and statistical results from multiple perspectives with several state-of-the-art (SOTA) algorithms, the results show that the proposed MSCSO algorithm has good optimization ability and can adapt to a wide range of optimization problems.