258 research outputs found
Effect of Gelsemium elegans
Gelsemium elegans (GE) is a kind of well-known toxic plant. It can be detoxified by Mussaenda pubescens (MP), but the detoxification mechanism is still unclear. Thus, a detoxification herbal formula (GM) comprising GE and MP was derived. The Caco-2 cells monolayer model was used to evaluate GM effects on transporting six kinds of indole alkaloids of GE. The bidirectional transport studies demonstrated that absorbance percentage of indole alkaloids in GE increased linearly over time. But in GM, Papp (AP→BL) values of the most toxic members, gelsenicine, humantenidine, and gelsevirine, were lower than that of Papp (BL→AP) (P<0.05). The prominent analgesic effect members, gelsemine and koumine, were approximately 1.00 in γ values. Nowhere was this increasing efflux more pronounced than in the case of indole alkaloids with N-O structure. In the presence of verapamil, the γ values of humantenidine, gelsenicine, gelsevirine, and humantenine were decreased by 43.69, 41.42, 36.00, and 8.90 percent, respectively. The γ values in presence of ciclosporin were homologous with a decrease of 42.32, 40.59, 34.00, and 15.07 percent. It suggested that the efflux transport was affected by transporters. Taken together, due to the efflux transporters participation, the increasing efflux of indole alkaloids from GM was found in Caco-2 cells
In-Domain GAN Inversion for Faithful Reconstruction and Editability
Generative Adversarial Networks (GANs) have significantly advanced image
synthesis through mapping randomly sampled latent codes to high-fidelity
synthesized images. However, applying well-trained GANs to real image editing
remains challenging. A common solution is to find an approximate latent code
that can adequately recover the input image to edit, which is also known as GAN
inversion. To invert a GAN model, prior works typically focus on reconstructing
the target image at the pixel level, yet few studies are conducted on whether
the inverted result can well support manipulation at the semantic level. This
work fills in this gap by proposing in-domain GAN inversion, which consists of
a domain-guided encoder and a domain-regularized optimizer, to regularize the
inverted code in the native latent space of the pre-trained GAN model. In this
way, we manage to sufficiently reuse the knowledge learned by GANs for image
reconstruction, facilitating a wide range of editing applications without any
retraining. We further make comprehensive analyses on the effects of the
encoder structure, the starting inversion point, as well as the inversion
parameter space, and observe the trade-off between the reconstruction quality
and the editing property. Such a trade-off sheds light on how a GAN model
represents an image with various semantics encoded in the learned latent
distribution. Code, models, and demo are available at the project page:
https://genforce.github.io/idinvert/
Improving 3D-aware Image Synthesis with A Geometry-aware Discriminator
3D-aware image synthesis aims at learning a generative model that can render
photo-realistic 2D images while capturing decent underlying 3D shapes. A
popular solution is to adopt the generative adversarial network (GAN) and
replace the generator with a 3D renderer, where volume rendering with neural
radiance field (NeRF) is commonly used. Despite the advancement of synthesis
quality, existing methods fail to obtain moderate 3D shapes. We argue that,
considering the two-player game in the formulation of GANs, only making the
generator 3D-aware is not enough. In other words, displacing the generative
mechanism only offers the capability, but not the guarantee, of producing
3D-aware images, because the supervision of the generator primarily comes from
the discriminator. To address this issue, we propose GeoD through learning a
geometry-aware discriminator to improve 3D-aware GANs. Concretely, besides
differentiating real and fake samples from the 2D image space, the
discriminator is additionally asked to derive the geometry information from the
inputs, which is then applied as the guidance of the generator. Such a simple
yet effective design facilitates learning substantially more accurate 3D
shapes. Extensive experiments on various generator architectures and training
datasets verify the superiority of GeoD over state-of-the-art alternatives.
Moreover, our approach is registered as a general framework such that a more
capable discriminator (i.e., with a third task of novel view synthesis beyond
domain classification and geometry extraction) can further assist the generator
with a better multi-view consistency.Comment: Accepted by NeurIPS 2022. Project page:
https://vivianszf.github.io/geo
Leveraging Prototype Patient Representations with Feature-Missing-Aware Calibration to Mitigate EHR Data Sparsity
Electronic Health Record (EHR) data frequently exhibits sparse
characteristics, posing challenges for predictive modeling. Current direct
imputation such as matrix imputation approaches hinge on referencing analogous
rows or columns to complete raw missing data and do not differentiate between
imputed and actual values. As a result, models may inadvertently incorporate
irrelevant or deceptive information with respect to the prediction objective,
thereby compromising the efficacy of downstream performance. While some methods
strive to recalibrate or augment EHR embeddings after direct imputation, they
often mistakenly prioritize imputed features. This misprioritization can
introduce biases or inaccuracies into the model. To tackle these issues, our
work resorts to indirect imputation, where we leverage prototype
representations from similar patients to obtain a denser embedding. Recognizing
the limitation that missing features are typically treated the same as present
ones when measuring similar patients, our approach designs a feature confidence
learner module. This module is sensitive to the missing feature status,
enabling the model to better judge the reliability of each feature. Moreover,
we propose a novel patient similarity metric that takes feature confidence into
account, ensuring that evaluations are not based merely on potentially
inaccurate imputed values. Consequently, our work captures dense prototype
patient representations with feature-missing-aware calibration process.
Comprehensive experiments demonstrate that designed model surpasses established
EHR-focused models with a statistically significant improvement on MIMIC-III
and MIMIC-IV datasets in-hospital mortality outcome prediction task. The code
is publicly available at \url{https://github.com/yhzhu99/SparseEHR} to assure
the reproducibility
MusiLingo: Bridging Music and Text with Pre-trained Language Models for Music Captioning and Query Response
Large Language Models (LLMs) have shown immense potential in multimodal
applications, yet the convergence of textual and musical domains remains
relatively unexplored. To address this gap, we present MusiLingo, a novel
system for music caption generation and music-related query responses.
MusiLingo employs a single projection layer to align music representations from
the pre-trained frozen music audio model MERT with the frozen LLaMA language
model, bridging the gap between music audio and textual contexts. We train it
on an extensive music caption dataset and fine-tune it with instructional data.
Due to the scarcity of high-quality music Q&A datasets, we created the
MusicInstruct (MI) dataset from MusicCaps, tailored for open-ended music
inquiries. Empirical evaluations demonstrate its competitive performance in
generating music captions and composing music-related Q&A pairs. Our introduced
dataset enables notable advancements beyond previous ones
Gaussian Shell Maps for Efficient 3D Human Generation
Efficient generation of 3D digital humans is important in several industries,
including virtual reality, social media, and cinematic production. 3D
generative adversarial networks (GANs) have demonstrated state-of-the-art
(SOTA) quality and diversity for generated assets. Current 3D GAN
architectures, however, typically rely on volume representations, which are
slow to render, thereby hampering the GAN training and requiring
multi-view-inconsistent 2D upsamplers. Here, we introduce Gaussian Shell Maps
(GSMs) as a framework that connects SOTA generator network architectures with
emerging 3D Gaussian rendering primitives using an articulable multi
shell--based scaffold. In this setting, a CNN generates a 3D texture stack with
features that are mapped to the shells. The latter represent inflated and
deflated versions of a template surface of a digital human in a canonical body
pose. Instead of rasterizing the shells directly, we sample 3D Gaussians on the
shells whose attributes are encoded in the texture features. These Gaussians
are efficiently and differentiably rendered. The ability to articulate the
shells is important during GAN training and, at inference time, to deform a
body into arbitrary user-defined poses. Our efficient rendering scheme bypasses
the need for view-inconsistent upsamplers and achieves high-quality multi-view
consistent renderings at a native resolution of pixels. We
demonstrate that GSMs successfully generate 3D humans when trained on
single-view datasets, including SHHQ and DeepFashion.Comment: Project page : https://rameenabdal.github.io/GaussianShellMaps
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