Analysis of Down syndrome failed to be diagnosed after prenatal screening: A multicenter study.

To analyze the characters of Down syndrome (DS) who failed to be diagnosed after prenatal screening and hope to be able to improve the programs of prenatal screening and reduce the missed diagnosis of DS. In this multicenter study, we collected the missed cases from 3 prenatal diagnosis centers and analyzed their characters. A total of 126 DS babies failed to be diagnosed after prenatal screening. Their mothers accepted the prenatal screening in second trimester. We collected the mothers' blood and detected the levels of alpha-fetoprotein (AFP) and the free beta subunit of human chorionic gonadotropin (fβhCG) by time-resolved fluoroimmunoassay. The values were also presented as multiples of the median (MoM) and determined the risk of carrying a fetus with DS by Wallace LifeCycle Elipse analysis software. Compared with normal control group, the level of fβhCG and hCG MoM were dramatically increased, while AFP and AFP MoM were decreased. The area under the receiver-operating-characteristic curve of trisomy 21 was 0.8387 for hCG-MoM and AFP-MoM testing. The sensitivity, specificity, positive predictive value, and negative predictive value were 84.6%, 74.8%, 75.4%, and 83.6%, respectively. Meanwhile, the prediction mode was "0.39957 + 1.90897HCG-MOM -3.32713AFP-MOM". It was worthwhile noting that the risk of 65.9% DS missed diagnosis group were higher than 1/1000, 92.9% higher than 1/3000. However, 72.5% cases in normal control group were lower than 1/3000. Only 9.2% mothers would be higher than the value of risk in 1/1000. The prediction mode of hCG MoM and AFP MoM might be able to help us reduce the missed diagnosis. It is also necessary to adjust more reasonable range of noninvasive prenatal testing with further clinical researches

Practices and Insights of Digital Transformation in Financial Management Education at Private Universities

This paper aims to explore the practical experiences and insights gained from digital transformation initiatives in financial management education at private universities through case studies. The widespread application of digital technology in the field of education is profoundly altering conventional teaching methodologies. By conducting thorough investigations into the digital transformation endeavors within financial management education at various private universities, this study compiles the key success factors evident in effective cases, such as collaborative efforts among faculty members and increased student engagement. Moreover, valuable recommendations are drawn from these experiences, including suggestions to enhance teacher training and optimize course content. These practical insights and lessons have significant implications for similar disciplines and contribute valuable guidance to the process of digital transformation within higher education institutions

Phonon and Raman scattering of two-dimensional transition metal dichalcogenides from monolayer, multilayer to bulk material

Two-dimensional (2D) transition metal dichalcogenide (TMD) nanosheets exhibit remarkable electronic and optical properties. The 2D features, sizable bandgaps, and recent advances in the synthesis, characterization, and device fabrication of the representative MoS$_2$, WS$_2$, WSe$_2$, and MoSe$_2$ TMDs make TMDs very attractive in nanoelectronics and optoelectronics. Similar to graphite and graphene, the atoms within each layer in 2D TMDs are joined together by covalent bonds, while van der Waals interactions keep the layers together. This makes the physical and chemical properties of 2D TMDs layer dependent. In this review, we discuss the basic lattice vibrations of monolayer, multilayer, and bulk TMDs, including high-frequency optical phonons, interlayer shear and layer breathing phonons, the Raman selection rule, layer-number evolution of phonons, multiple phonon replica, and phonons at the edge of the Brillouin zone. The extensive capabilities of Raman spectroscopy in investigating the properties of TMDs are discussed, such as interlayer coupling, spin--orbit splitting, and external perturbations. The interlayer vibrational modes are used in rapid and substrate-free characterization of the layer number of multilayer TMDs and in probing interface coupling in TMD heterostructures. The success of Raman spectroscopy in investigating TMD nanosheets paves the way for experiments on other 2D crystals and related van der Waals heterostructures.Comment: 30 pages, 23 figure

Rethinking Batch Sample Relationships for Data Representation: A Batch-Graph Transformer based Approach

Exploring sample relationships within each mini-batch has shown great potential for learning image representations. Existing works generally adopt the regular Transformer to model the visual content relationships, ignoring the cues of semantic/label correlations between samples. Also, they generally adopt the "full" self-attention mechanism which are obviously redundant and also sensitive to the noisy samples. To overcome these issues, in this paper, we design a simple yet flexible Batch-Graph Transformer (BGFormer) for mini-batch sample representations by deeply capturing the relationships of image samples from both visual and semantic perspectives. BGFormer has three main aspects. (1) It employs a flexible graph model, termed Batch Graph to jointly encode the visual and semantic relationships of samples within each mini-batch. (2) It explores the neighborhood relationships of samples by borrowing the idea of sparse graph representation which thus performs robustly, w.r.t., noisy samples. (3) It devises a novel Transformer architecture that mainly adopts dual structure-constrained self-attention (SSA), together with graph normalization, FFN, etc, to carefully exploit the batch graph information for sample tokens (nodes) representations. As an application, we apply BGFormer to the metric learning tasks. Extensive experiments on four popular datasets demonstrate the effectiveness of the proposed model

Deterministic versus probabilistic quantum information masking

We investigate quantum information masking for arbitrary dimensional quantum states. We show that mutually orthogonal quantum states can always be served for deterministic masking of quantum information. We further construct a probabilistic masking machine for linearly independent states. It is shown that a set of d dimensional states, $\{ |a_1 \rangle_A, |t a_2 \rangle_A, \dots, |a_n \rangle_A \}$, $n \leq d$, can be probabilistically masked by a general unitary-reduction operation if they are linearly independent. The maximal successful probability of probabilistic masking is analyzed and derived for the case of two initial states.Comment: 5 pages, 1 figure