Sub-token ViT Embedding via Stochastic Resonance Transformers

We discover the presence of quantization artifacts in Vision Transformers (ViTs), which arise due to the image tokenization step inherent in these architectures. These artifacts result in coarsely quantized features, which negatively impact performance, especially on downstream dense prediction tasks. We present a zero-shot method to improve how pre-trained ViTs handle spatial quantization. In particular, we propose to ensemble the features obtained from perturbing input images via sub-token spatial translations, inspired by Stochastic Resonance, a method traditionally applied to climate dynamics and signal processing. We term our method ``Stochastic Resonance Transformer" (SRT), which we show can effectively super-resolve features of pre-trained ViTs, capturing more of the local fine-grained structures that might otherwise be neglected as a result of tokenization. SRT can be applied at any layer, on any task, and does not require any fine-tuning. The advantage of the former is evident when applied to monocular depth prediction, where we show that ensembling model outputs are detrimental while applying SRT on intermediate ViT features outperforms the baseline models by an average of 4.7% and 14.9% on the RMSE and RMSE-log metrics across three different architectures. When applied to semi-supervised video object segmentation, SRT also improves over the baseline models uniformly across all metrics, and by an average of 2.4% in F&J score. We further show that these quantization artifacts can be attenuated to some extent via self-distillation. On the unsupervised salient region segmentation, SRT improves upon the base model by an average of 2.1% on the maxF metric. Finally, despite operating purely on pixel-level features, SRT generalizes to non-dense prediction tasks such as image retrieval and object discovery, yielding consistent improvements of up to 2.6% and 1.0% respectively

Isolation of Endogenously Assembled RNA-Protein Complexes Using Affinity Purification Based on Streptavidin Aptamer S1

Efficient isolation of endogenously assembled viral RNA-protein complexes is essential for understanding virus replication mechanisms. We have developed an affinity purification strategy based on an RNA affinity tag that allows large-scale preparation of native viral RNA-binding proteins (RBPs). The streptavidin-binding aptamer S1 sequence was inserted into the 3′ end of dengue virus (DENV) 5′–3′ UTR RNA, and the DENV RNA UTR fused to the S1 RNA aptamer was expressed in living mammalian cells. This allowed endogenous viral ribonucleoprotein (RNP) assembly and isolation of RNPs from whole cell extract, through binding the S1 aptamer to streptavidin magnetic beads. Several novel host DENV RBPs were subsequently identified by liquid chromatography with tandem mass spectrometry (LC-MS/MS), including RPS8, which we further implicate in DENV replication. We proposed efficient S1 aptamer-based isolation of viral assembled RNPs from living mammalian cells will be generally applicable to the purification of high- and low-affinity RBPs and RNPs under endogenous conditions

Protein Microspheres with Unique Green and Red Autofluorescence for Noninvasively Tracking and Modeling Their in Vivo Biodegradation

Bovine serum albumin (BSA) microspheres were prepared through a facile and low-cost route including a high-speed dispersion of BSA in cross-linking solution followed by spray drying. Interestingly the as-prepared BSA microspheres possess unique blue-green, green, green-yellow, and red fluorescence when excited by specific wavelengths of laser or LED light. The studies of UV–visible reflectance spectra and fluorescence emission spectra indicated that four classes of fluorescent compounds are presumably formed during the fabrication processes. The formation and the potential contributors for the unique green and red autofluorescence were also discussed and proposed though the exact structures of the fluorophores formed remain elusive due to the complexity of the protein system. The effect of spray-drying conditions on the morphology of spray-dried samples was investigated and optimized. FTIR was further employed to characterize the formation of the functional groups in the as-prepared autofluorescent microspheres. Good in vitro and in vivo biocompatibility was demonstrated by the cytotoxicity test on the A549 cancer cells and tissue histological analysis, respectively. The autofluorescent BSA microspheres themselves were then applied as a novel tracer for convenient tracking/modeling of the biodegradation of autofluorescent BSA microspheres injected into mouse model based on noninvasive, time-dependent fluorescence images of the mice, in which experimental data are in good agreement with the proposed mathematical model. All these studies indicate that the as-developed protein microspheres exhibiting good biocompatibility, biodegradability, and unique autofluorescence, can significantly broaden biomedical applications of fluorescent protein particles