Clinical Application of In Vitro Maturation of Oocytes

In vitro maturation (IVM) is a technique used to induce immature oocytes collected in different periods of embryonic growth. The rates vary for immature oocytes collected from different clinical sources to potentially develop into embryos and achieve live birth. As an effective treatment method, IVM can be used to treat patients with polycystic ovary syndrome (PCOS), ovarian hyperresponsiveness, and hyporesponsiveness, as well as to preserve the fertility of cancer patients. This technology has been used worldwide for the birth of thousands of healthy babies. The improvement in clinical IVM technology mainly focuses on the IVM medium and the optimization of the culture environment and operation process. At present, with the improvement in the in vitro fertilization (IVF) efficiency and culture systems, a natural cycle or mild stimulation may be more suitable for women receiving IVF treatments. A new treatment option was proposed to combine natural cycle/mild stimulation IVF with IVM. In particular, the combination of mild stimulation IVF and IVM is not only expected to become a viable alternative to current standard treatments but may also become a potential option of first-line treatment

Revisiting energy efficiency and energy related CO2 emissions: Evidence from RCEP economies

Since the last four decades, energy demand has been reached to the utmost level, which also leads to emissions and causes environmental degradation, global warming and climate change all over the world. In this sense, policy makers have suggested various measures including renewable adoption and energy efficiency. Current study aims to investigate the influence of economic growth, energy consumption, renewable electricity output, and energy efficiency on the energy related emissions. A panel of 12 RCEP economies are examined covering the period 1990-2020. Since the data follows irregular path, therefore a novel method of moment panel quantile regression is employed along with the Granger causality test. The empirical results indicate that economic growth and energy consumption significantly enhances energy related emissions, where the magnitude and significance level is found strengthening from lower to upper quantiles (Q0.25, Q0.50, Q0.75 and Q0.90). Conversely, renewable electricity and energy efficiency are the significant tools for lowering energy related emissions in the region. Additionally, a unidirectional causality is found from energy consumption and renewable electricity output to energy related emissions. However, a feedback effect is validated between economic growth, energy efficiency, and energy related emissions. Based on the empirical findings, this study suggests enhancement of renewable electricity output and adoption of energy efficient technologies to reduce environmental degradation and emission level

Hyper Association Graph Matching with Uncertainty Quantification for Coronary Artery Semantic Labeling

Coronary artery disease (CAD) is one of the primary causes leading to death worldwide. Accurate extraction of individual arterial branches on invasive coronary angiograms (ICA) is important for stenosis detection and CAD diagnosis. However, deep learning-based models face challenges in generating semantic segmentation for coronary arteries due to the morphological similarity among different types of coronary arteries. To address this challenge, we propose an innovative approach using the hyper association graph-matching neural network with uncertainty quantification (HAGMN-UQ) for coronary artery semantic labeling on ICAs. The graph-matching procedure maps the arterial branches between two individual graphs, so that the unlabeled arterial segments are classified by the labeled segments, and the coronary artery semantic labeling is achieved. By incorporating the anatomical structural loss and uncertainty, our model achieved an accuracy of 0.9345 for coronary artery semantic labeling with a fast inference speed, leading to an effective and efficient prediction in real-time clinical decision-making scenarios.Comment: 8 pages, 2 figure

Discovering Low-rank Subspaces for Language-agnostic Multilingual Representations

Large pretrained multilingual language models (ML-LMs) have shown remarkable capabilities of zero-shot cross-lingual transfer, without direct cross-lingual supervision. While these results are promising, follow-up works found that, within the multilingual embedding spaces, there exists strong language identity information which hinders the expression of linguistic factors shared across languages. For semantic tasks like cross-lingual sentence retrieval, it is desired to remove such language identity signals to fully leverage semantic information. In this work, we provide a novel view of projecting away language-specific factors from a multilingual embedding space. Specifically, we discover that there exists a low-rank subspace that primarily encodes information irrelevant to semantics (e.g., syntactic information). To identify this subspace, we present a simple but effective unsupervised method based on singular value decomposition with multiple monolingual corpora as input. Once the subspace is found, we can directly project the original embeddings into the null space to boost language agnosticism without finetuning. We systematically evaluate our method on various tasks including the challenging language-agnostic QA retrieval task. Empirical results show that applying our method consistently leads to improvements over commonly used ML-LMs.Comment: 17 pages, 7 figures, EMNLP 2022 (main conference

Ticking terahertz wave generation in attoseconds

We perform a joint measurement of terahertz waves and high-order harmonics generated from noble atoms driven by a fundamental laser pulse and its second harmonic. By correlating their dependence on the phase-delay of the two pulses, we determine the generation of THz waves in tens of attoseconds precision. Compared with simulations and models, we find that the laser-assisted soft-collision of the electron wave packet with the atomic core plays a key role. It is demonstrated that the rescattering process, being indispensable in HHG processes, dominant THz wave generation as well but in a more elaborate way. The new finding might be helpful for the full characterization of the rescattering dynamics.Comment: 4 figure