UpCycling: Semi-supervised 3D Object Detection without Sharing Raw-level Unlabeled Scenes

Semi-supervised Learning (SSL) has received increasing attention in autonomous driving to relieve enormous burden for 3D annotation. In this paper, we propose UpCycling, a novel SSL framework for 3D object detection with zero additional raw-level point cloud: learning from unlabeled de-identified intermediate features (i.e., smashed data) for privacy preservation. The intermediate features do not require additional computation on autonomous vehicles since they are naturally produced by the inference pipeline. However, augmenting 3D scenes at a feature level turns out to be a critical issue: applying the augmentation methods in the latest semi-supervised 3D object detectors distorts intermediate features, which causes the pseudo-labels to suffer from significant noise. To solve the distortion problem while achieving highly effective SSL, we introduce hybrid pseudo labels, feature-level Ground Truth sampling (F-GT) and Rotation (F-RoT), which safely augment unlabeled multi-type 3D scene features and provide high-quality supervision. We implement UpCycling on two representative 3D object detection models, SECOND-IoU and PV-RCNN, and perform experiments on widely-used datasets (Waymo, KITTI, and Lyft). While preserving privacy with zero raw-point scene, UpCycling significantly outperforms the state-of-the-art SSL methods that utilize raw-point scenes, in both domain adaptation and partial-label scenarios

Characterization of Phosphorylated Tau-Microtubule complex with Molecular Dynamics (MD) simulation

Alzheimer's Disease (AD), a neurodegenerative disorder, is reported as one of the most severe health and socioeconomic problems in current public health. Tau proteins are assumed to be a crucial driving factor of AD that detach from microtubules (MT) and accumulate as neurotoxic aggregates in the brains of AD patients. Extensive experimental and computational research has observed that phosphorylation at specific tau residues enhances aggregation, but the exact mechanisms underlying this phenomenon remain unclear. In this study, we employed molecular dynamics (MD) simulations on pseudo-phosphorylated tau-MT complex (residue 199 ~ 312), incorporating structural data from recent cryo-electron microscopy studies. Simulation results have revealed altered tau conformations after applying pseudo-phosphorylation. Additionally, root-mean-square deviation (RMSD) analyses and dimensionality reduction of dihedral angles revealed key residues responsible for these conformational shiftsComment: 27pages, 12 figur

PeptideBERT: A Language Model based on Transformers for Peptide Property Prediction

Recent advances in Language Models have enabled the protein modeling community with a powerful tool since protein sequences can be represented as text. Specifically, by taking advantage of Transformers, sequence-to-property prediction will be amenable without the need for explicit structural data. In this work, inspired by recent progress in Large Language Models (LLMs), we introduce PeptideBERT, a protein language model for predicting three key properties of peptides (hemolysis, solubility, and non-fouling). The PeptideBert utilizes the ProtBERT pretrained transformer model with 12 attention heads and 12 hidden layers. We then finetuned the pretrained model for the three downstream tasks. Our model has achieved state of the art (SOTA) for predicting Hemolysis, which is a task for determining peptide's potential to induce red blood cell lysis. Our PeptideBert non-fouling model also achieved remarkable accuracy in predicting peptide's capacity to resist non-specific interactions. This model, trained predominantly on shorter sequences, benefits from the dataset where negative examples are largely associated with insoluble peptides. Codes, models, and data used in this study are freely available at: https://github.com/ChakradharG/PeptideBERTComment: 24 page

Association of the Apolipoprotein A5 Gene −1131T>C Polymorphism with Serum Lipids in Korean Subjects: Impact of Sasang Constitution

Apolipoprotein A5 (APOA5) was identified as a strong modulator of serum lipids. Moreover, an APOA5 gene −1131T>C polymorphism has been associated with serum lipids, but the results are inconsistent according to ethnic and racial groups. We have genotyped and analyzed 1,619 outpatients of Korean oriental medicine hospitals who were classified into three Sasang constitution groups (SCGs), So-Yang (SY), So-Eum (SE), and Tae-Eum (TE). There were no significant difference in the distribution of the APOA5 −1131T>C genotype among the three SCGs. Subjects with the C allele in SY and TE showed significantly lower serum high-density lipoprotein cholesterol (HDL-C) and higher triglyceride (TG) levels than noncarriers of the C allele. These results show the differences in the prevalence of decreasing serum HDL-C and elevating serum TG levels along with APOA5 −1131T>C polymorphism according to SCG and suggest that SCG may act as a significant risk factor for hypo-HDL-C-emia and hypertriglyceridemia susceptibility

GPCR-BERT: Interpreting Sequential Design of G Protein Coupled Receptors Using Protein Language Models

With the rise of Transformers and Large Language Models (LLMs) in Chemistry and Biology, new avenues for the design and understanding of therapeutics have opened up to the scientific community. Protein sequences can be modeled as language and can take advantage of recent advances in LLMs, specifically with the abundance of our access to the protein sequence datasets. In this paper, we developed the GPCR-BERT model for understanding the sequential design of G Protein-Coupled Receptors (GPCRs). GPCRs are the target of over one-third of FDA-approved pharmaceuticals. However, there is a lack of comprehensive understanding regarding the relationship between amino acid sequence, ligand selectivity, and conformational motifs (such as NPxxY, CWxP, E/DRY). By utilizing the pre-trained protein model (Prot-Bert) and fine-tuning with prediction tasks of variations in the motifs, we were able to shed light on several relationships between residues in the binding pocket and some of the conserved motifs. To achieve this, we took advantage of attention weights, and hidden states of the model that are interpreted to extract the extent of contributions of amino acids in dictating the type of masked ones. The fine-tuned models demonstrated high accuracy in predicting hidden residues within the motifs. In addition, the analysis of embedding was performed over 3D structures to elucidate the higher-order interactions within the conformations of the receptors.Comment: 25 pages, 5 figure

Fabrication of double-ceramic-layer TBCs by suspension plasma spray

Rare-earth zirconates, such as La2Zr2O7 and Gd2Zr2O7, have been investigated as one of the candidates for replacing conventional yttria-stabilized zirconia (YSZ) for thermal barrier coating (TBC) applications at higher turbine inlet temperatures. Rare-earth zirconate oxides exhibit little phase transformation upon heating up to melting temperature as well as low thermal conductivity, where as their mechanical properties is inferior to those of YSZ TBCs. Double-ceramic-layer (DCL) TBCs have been investigated in order to take advantage of beneficial characteristics of both YSZ and rare-earth zirconate. In this study, the fabrication of DCL-TBCs with YSZ layer and rare-earth-zirconate top layer by using suspension plasma spray are reported. Microstructure, compositional profile, thermal conductivity, and thermal durability of DCL-TBCs are characterized. The usefulness of these DCL-TBCs is also discussed

Boosting Up the Low Catalytic Activity of Silver for H2 Production on Ag/TiO2 Photocatalyst: Thiocyanate as a Selective Modifier

Noble metal cocatalysts like Pt have been widely employed as an essential ingredient in many kinds of photocatalytic materials for solar hydrogen production. The high material cost of Pt is the biggest limitation. Silver is far less expensive but much less active than Pt and Au as a hydrogen evolving catalyst. Here we demonstrate a new strategy to boost up the activity of silver in Ag/TiO2 for photocatalytic H-2 production via forming a simple surface complexation of thiocyanate (SCN-) on silver. The addition of thiocyanate in the suspension of Ag/TiO2 markedly enhanced the photocatalytic production of H-2 by about 4 times. Thiocyanate was not consumed at all during the photoreaction, which ruled out the role of thiocyanate as an electron donor. Such a positive role of thiocyanate was not observed with bare TiO2, Pt/TiO2, and Au/TiO2. The selective chemisorption of thiocyanate on silver was confirmed by the analyses of Raman spectroscopy and spot-profile energy-dispersive spectroscopy. In the presence of thiocyanate, the overpotential for water reduction on Ag/TiO2 electrode was slightly reduced, and the interfacial charge transfer resistance on Ag/TiO2 (measured by electrochemical impedance spectroscopy) was significantly decreased, whereas other electrode systems (bare TiO2, Au/TiO2, and Pt/TiO2) showed the opposite effect of thiocyanate. These results indicate that the adsorption of thiocyanate on Ag facilitates the transfer of photogenerated electrons on the Ag/TiO2 electrode. It is proposed that the formation of Ag-SCN surface complex enhances the interfacial electron transfer rate and facilitates the reduction of protons on Ag/TiO2.115640Ysciescopu