351 research outputs found
Luteinizing Hormone Action in Human Oocyte Maturation and Quality: Signaling Pathways, Regulation, and Clinical Impact
The ovarian follicle luteinizing hormone (LH) signaling molecules that regulate oocyte meiotic maturation have recently been identified. The LH signal reduces preovulatory follicle cyclic nucleotide levels which releases oocytes from the first meiotic arrest. In the ovarian follicle, the LH signal reduces cyclic nucleotide levels via the CNP/NPR2 system, the EGF/EGF receptor network, and follicle/oocyte gap junctions. In the oocyte, reduced cyclic nucleotide levels activate the maturation promoting factor (MPF). The activated MPF induces chromosome segregation and completion of the first and second meiotic divisions. The purpose of this paper is to present an overview of the current understanding of human LH signaling regulation of oocyte meiotic maturation by identifying and integrating the human studies on this topic. We found 89 human studies in the literature that identified 24 LH follicle/oocyte signaling proteins. These studies show that human oocyte meiotic maturation is regulated by the same proteins that regulate animal oocyte meiotic maturation. We also found that these LH signaling pathway molecules regulate human oocyte quality and subsequent embryo quality. Remarkably, in vitro maturation (IVM) prematuration culture (PMC) protocols that manipulate the LH signaling pathway improve human oocyte quality of cultured human oocytes. This knowledge has improved clinical human IVM efficiency which may become a routine alternative ART for some infertile patients
Unpaired Image-to-Image Translation via Neural Schr\"odinger Bridge
Diffusion models are a powerful class of generative models which simulate
stochastic differential equations (SDEs) to generate data from noise. Although
diffusion models have achieved remarkable progress in recent years, they have
limitations in the unpaired image-to-image translation tasks due to the
Gaussian prior assumption. Schr\"odinger Bridge (SB), which learns an SDE to
translate between two arbitrary distributions, have risen as an attractive
solution to this problem. However, none of SB models so far have been
successful at unpaired translation between high-resolution images. In this
work, we propose the Unpaired Neural Schr\"odinger Bridge (UNSB), which
combines SB with adversarial training and regularization to learn a SB between
unpaired data. We demonstrate that UNSB is scalable, and that it successfully
solves various unpaired image-to-image translation tasks. Code:
\url{https://github.com/cyclomon/UNSB
Bridging Active Exploration and Uncertainty-Aware Deployment Using Probabilistic Ensemble Neural Network Dynamics
In recent years, learning-based control in robotics has gained significant
attention due to its capability to address complex tasks in real-world
environments. With the advances in machine learning algorithms and
computational capabilities, this approach is becoming increasingly important
for solving challenging control problems in robotics by learning unknown or
partially known robot dynamics. Active exploration, in which a robot directs
itself to states that yield the highest information gain, is essential for
efficient data collection and minimizing human supervision. Similarly,
uncertainty-aware deployment has been a growing concern in robotic control, as
uncertain actions informed by the learned model can lead to unstable motions or
failure. However, active exploration and uncertainty-aware deployment have been
studied independently, and there is limited literature that seamlessly
integrates them. This paper presents a unified model-based reinforcement
learning framework that bridges these two tasks in the robotics control domain.
Our framework uses a probabilistic ensemble neural network for dynamics
learning, allowing the quantification of epistemic uncertainty via Jensen-Renyi
Divergence. The two opposing tasks of exploration and deployment are optimized
through state-of-the-art sampling-based MPC, resulting in efficient collection
of training data and successful avoidance of uncertain state-action spaces. We
conduct experiments on both autonomous vehicles and wheeled robots, showing
promising results for both exploration and deployment.Comment: 2023 Robotics: Science and Systems (RSS). Project page:
https://taekyung.me/rss2023-bridgin
MarioNETte: Few-shot Face Reenactment Preserving Identity of Unseen Targets
When there is a mismatch between the target identity and the driver identity,
face reenactment suffers severe degradation in the quality of the result,
especially in a few-shot setting. The identity preservation problem, where the
model loses the detailed information of the target leading to a defective
output, is the most common failure mode. The problem has several potential
sources such as the identity of the driver leaking due to the identity
mismatch, or dealing with unseen large poses. To overcome such problems, we
introduce components that address the mentioned problem: image attention block,
target feature alignment, and landmark transformer. Through attending and
warping the relevant features, the proposed architecture, called MarioNETte,
produces high-quality reenactments of unseen identities in a few-shot setting.
In addition, the landmark transformer dramatically alleviates the identity
preservation problem by isolating the expression geometry through landmark
disentanglement. Comprehensive experiments are performed to verify that the
proposed framework can generate highly realistic faces, outperforming all other
baselines, even under a significant mismatch of facial characteristics between
the target and the driver.Comment: In AAAI 202
Experimental investigations on the characteristics of snow accretion using the EMU-320 model train
This paper presents a snow accretion test conducted in a climate wind tunnel
to investigate the icing process on a model train. The model used within this
experiment was the cleaned-up and 2/3-scaled version of EMU-320, which is a
high-speed train in Korea. The model was designed without an electronic power
source or heat source so that the wheels did not rotate and snow accretion on
the model did not occur due to heat sources. To investigate snow accretion,
four cases with different ambient temperatures were considered in the climate
wind tunnel on Rail Tec Arsenal. Before analyzing the snow accretion on the
train, the snow flux and liquid water content of snow were measured so that
they could be used as the input conditions for the simulation and to ensure the
analysis of the icing process was based on the characteristics of the snow.
Both qualitative and quantitative data were obtained, whereby photographs was
used for qualitative analysis, and the density of the snow sample and the
thickness of snow accreted on the model were used for quantitative analysis.
Based on the visual observations, it was deduced that as the ambient
temperature increased, the range of the snow accreted was broader. The
thickness of snow accreted on the model nose was the largest on the upper and
lower part at -3 oC, and on the middle part at -5 oC. Additionally, the cross
section of snow accreted was observed to be trench-like. Similar icing
processes were observed to occur on the slope of nose. Snow accreted on all
components of the bogie, and for all cases, the thickness of snow at wheel was
the largest at an arc angle of 40 to 70 o. These detailed data of experimental
conditions can be applied as an input to simulations to improve simulations of
ice conditions. Thus, they can facilitate the development of appropriate
anti-icing designs for trainsComment: 31 pages, 23 Figures, 8 Table
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