1,035 research outputs found
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
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