Visualization tools can help synthetic biologists and molecular programmers
understand the complex reactive pathways of nucleic acid reactions, which can
be designed for many potential applications and can be modelled using a
continuous-time Markov chain (CTMC). Here we present ViDa, a new visualization
approach for DNA reaction trajectories that uses a 2D embedding of the
secondary structure state space underlying the CTMC model. To this end, we
integrate a scattering transform of the secondary structure adjacency, a
variational autoencoder, and a nonlinear dimensionality reduction method. We
augment the training loss with domain-specific supervised terms that capture
both thermodynamic and kinetic features. We assess ViDa on two well-studied DNA
hybridization reactions. Our results demonstrate that the domain-specific
features lead to significant quality improvements over the state-of-the-art in
DNA state space visualization, successfully separating different folding
pathways and thus providing useful insights into dominant reaction mechanisms.

ViDa 是一种新的 DNA 反应轨迹可视化方法，通过二维嵌入连续时间马尔可夫链模型下的次级结构状态空间，结合散射变换、变分自动编码器和非线性降维方法，增强域特定的监督术语捕捉热力学和动力学特征，明显提高 DNA 状态空间可视化的质量，成功区分不同的折叠途径，从而为主要反应机制提供有用的见解。

ViDa: 利用生物物理学知识的深度图嵌入可视化 DNA 杂交轨迹

ViDa: Visualizing DNA hybridization trajectories with  biophysics-informed deep graph embeddings

Since real-world machine systems are running in non-stationary and
continually changing environments, Continual Test-Time Adaptation (CTTA) task
is proposed to adapt the pre-trained model to continually changing target
domains. Recently, existing methods mainly focus on model-based adaptation,
which aims to leverage a self-training manner to extract the target domain
knowledge. However, pseudo labels can be noisy and the updated model parameters
are uncertain under dynamic data distributions, leading to error accumulation
and catastrophic forgetting in the continual adaptation process. To tackle
these challenges and maintain the model plasticity, we tactfully design a
Visual Domain Adapter (ViDA) for CTTA, explicitly handling both domain-specific
and domain-agnostic knowledge. Specifically, we first comprehensively explore
the different domain representations of the adapters with trainable high and
low-rank embedding space. Then we inject ViDAs into the pre-trained model,
which leverages high-rank and low-rank prototypes to adapt the current domain
distribution and maintain the continual domain-shared knowledge, respectively.
To adapt to the various distribution shifts of each sample in target domains,
we further propose a Homeostatic Knowledge Allotment (HKA) strategy, which
adaptively merges knowledge from each ViDA with different rank prototypes.
Extensive experiments conducted on four widely-used benchmarks demonstrate that
our proposed method achieves state-of-the-art performance in both
classification and segmentation CTTA tasks. In addition, our method can be
regarded as a novel transfer paradigm and showcases promising results in
zero-shot adaptation of foundation models to continual downstream tasks and
distributions.

本研究提出 Continual Test-Time Adaptation (CTTA) 任务及 Visual Domain Adapter (ViDA) 实现了对预训练模型的修改，以适应不断变化的目标领域；同时还提出了 Homeostatic Knowledge Allotment (HKA) 策略，通过对各个 ViDA 分配不同等级的知识以适应实际应用中不同领域的变化，并在分类和分割任务等方面实现了最新的表现。