Can We Solve 3D Vision Tasks Starting from A 2D Vision Transformer?

Vision Transformers (ViTs) have proven to be effective, in solving 2D image understanding tasks by training over large-scale image datasets; and meanwhile as a somehow separate track, in modeling the 3D visual world too such as voxels or point clouds. However, with the growing hope that transformers can become the "universal" modeling tool for heterogeneous data, ViTs for 2D and 3D tasks have so far adopted vastly different architecture designs that are hardly transferable. That invites an (over-)ambitious question: can we close the gap between the 2D and 3D ViT architectures? As a piloting study, this paper demonstrates the appealing promise to understand the 3D visual world, using a standard 2D ViT architecture, with only minimal customization at the input and output levels without redesigning the pipeline. To build a 3D ViT from its 2D sibling, we "inflate" the patch embedding and token sequence, accompanied with new positional encoding mechanisms designed to match the 3D data geometry. The resultant "minimalist" 3D ViT, named Simple3D-Former, performs surprisingly robustly on popular 3D tasks such as object classification, point cloud segmentation and indoor scene detection, compared to highly customized 3D-specific designs. It can hence act as a strong baseline for new 3D ViTs. Moreover, we note that pursing a unified 2D-3D ViT design has practical relevance besides just scientific curiosity. Specifically, we demonstrate that Simple3D-Former naturally enables to exploit the wealth of pre-trained weights from large-scale realistic 2D images (e.g., ImageNet), which can be plugged in to enhancing the 3D task performance "for free"

ProtChatGPT: Towards Understanding Proteins with Large Language Models

Protein research is crucial in various fundamental disciplines, but understanding their intricate structure-function relationships remains challenging. Recent Large Language Models (LLMs) have made significant strides in comprehending task-specific knowledge, suggesting the potential for ChatGPT-like systems specialized in protein to facilitate basic research. In this work, we introduce ProtChatGPT, which aims at learning and understanding protein structures via natural languages. ProtChatGPT enables users to upload proteins, ask questions, and engage in interactive conversations to produce comprehensive answers. The system comprises protein encoders, a Protein-Language Pertaining Transformer (PLP-former), a projection adapter, and an LLM. The protein first undergoes protein encoders and PLP-former to produce protein embeddings, which are then projected by the adapter to conform with the LLM. The LLM finally combines user questions with projected embeddings to generate informative answers. Experiments show that ProtChatGPT can produce promising responses to proteins and their corresponding questions. We hope that ProtChatGPT could form the basis for further exploration and application in protein research. Code and our pre-trained model will be publicly available

Taking a Respite from Representation Learning for Molecular Property Prediction

Artificial intelligence (AI) has been widely applied in drug discovery with a major task as molecular property prediction. Despite the boom of AI techniques in molecular representation learning, some key aspects underlying molecular property prediction haven't been carefully examined yet. In this study, we conducted a systematic comparison on three representative models, random forest, MolBERT and GROVER, which utilize three major molecular representations, extended-connectivity fingerprints, SMILES strings and molecular graphs, respectively. Notably, MolBERT and GROVER, are pretrained on large-scale unlabelled molecule corpuses in a self-supervised manner. In addition to the commonly used MoleculeNet benchmark datasets, we also assembled a suite of opioids-related datasets for downstream prediction evaluation. We first conducted dataset profiling on label distribution and structural analyses; we also examined the activity cliffs issue in the opioids-related datasets. Then, we trained 4,320 predictive models and evaluated the usefulness of the learned representations. Furthermore, we explored into the model evaluation by studying the effect of statistical tests, evaluation metrics and task settings. Finally, we dissected the chemical space generalization into inter-scaffold and intra-scaffold generalization and measured prediction performance to evaluate model generalizbility under both settings. By taking this respite, we reflected on the key aspects underlying molecular property prediction, the awareness of which can, hopefully, bring better AI techniques in this field

Point Contrastive Prediction with Semantic Clustering for Self-Supervised Learning on Point Cloud Videos

We propose a unified point cloud video self-supervised learning framework for object-centric and scene-centric data. Previous methods commonly conduct representation learning at the clip or frame level and cannot well capture fine-grained semantics. Instead of contrasting the representations of clips or frames, in this paper, we propose a unified self-supervised framework by conducting contrastive learning at the point level. Moreover, we introduce a new pretext task by achieving semantic alignment of superpoints, which further facilitates the representations to capture semantic cues at multiple scales. In addition, due to the high redundancy in the temporal dimension of dynamic point clouds, directly conducting contrastive learning at the point level usually leads to massive undesired negatives and insufficient modeling of positive representations. To remedy this, we propose a selection strategy to retain proper negatives and make use of high-similarity samples from other instances as positive supplements. Extensive experiments show that our method outperforms supervised counterparts on a wide range of downstream tasks and demonstrates the superior transferability of the learned representations.Comment: Accepted by ICCV 202

Masked Spatio-Temporal Structure Prediction for Self-supervised Learning on Point Cloud Videos

Recently, the community has made tremendous progress in developing effective methods for point cloud video understanding that learn from massive amounts of labeled data. However, annotating point cloud videos is usually notoriously expensive. Moreover, training via one or only a few traditional tasks (e.g., classification) may be insufficient to learn subtle details of the spatio-temporal structure existing in point cloud videos. In this paper, we propose a Masked Spatio-Temporal Structure Prediction (MaST-Pre) method to capture the structure of point cloud videos without human annotations. MaST-Pre is based on spatio-temporal point-tube masking and consists of two self-supervised learning tasks. First, by reconstructing masked point tubes, our method is able to capture the appearance information of point cloud videos. Second, to learn motion, we propose a temporal cardinality difference prediction task that estimates the change in the number of points within a point tube. In this way, MaST-Pre is forced to model the spatial and temporal structure in point cloud videos. Extensive experiments on MSRAction-3D, NTU-RGBD, NvGesture, and SHREC'17 demonstrate the effectiveness of the proposed method.Comment: Accepted by ICCV 202

Molecular cloning and in silico analysis of the duck (Anas platyrhynchos) MEF2A gene cDNA and its expression profile in muscle tissues during fetal development

The role of myogenic enhancer transcription factor 2a (MEF2A) in avian muscle during fetal development is unknown. In this work, we cloned the duck MEF2A cDNA sequence (GenBank accession no. HM460752) and examined its developmental expression profiles in cardiac muscle, non-vascular smooth muscle and skeletal muscle. Duck MEF2A cDNA comprised 1479 bp encoding 492 amino acid residues. In silico analysis showed that MEF2A contained MADS (MCM1, AGAMOUS, DEFICIENS and SRF - serum response factor), MEF2 and mitogen-activated protein kinase (MAPK) transcription domains with high homology to related proteins in other species. Modified sites in these domains were conserved among species and several variants were found. Quantitative PCR showed that MEF2A was expressed in all three muscles at each developmental stage examined, with the expression in smooth muscle being higher than in the other muscles. These results indicate that the conserved domains of duck MEF2A, including the MADS and MEF2 domains, are important for MEF2A transcription factor function. The expression of MEF2A in duck smooth muscle and cardiac muscle suggests that MEF2A plays a role in these two tissues

Recent advances in copper-based catalysts for electrocatalytic CO2 reduction toward multi-carbon products

Electrocatalytic carbon dioxide reduction reaction (CO2RR) holds the promise of both overcoming the greenhouse effect and synthesizing a wealth of chemicals. Electrocatalytic CO2 reduction toward carbon-containing products, including C1 products (carbon monoxide, formic acid, etc), C2 products (ethylene, ethanol, etc.) and multi-carbon products (e.g., n-propanol), provides beneficial fuel and chemicals for industrial production. The complexity of the multi-proton transfer processes and difficulties of C-C coupling in electrochemical CO2 reduction toward multi-carbon(C2+) products have attracted increasing concerns on the design of catalysts in comparison with those of C1 products. In this paper, we review the main advances in the syntheses of multi-carbon products through electrocatalytic carbon dioxide reduction in recent years, introduce the basic principles of electrocatalytic CO2RR, and detailly elucidate two widely accepted mechanisms of C-C coupling reactions. Among abundant nanomaterials, copper-based catalysts are outstanding catalysts for the preparation of multi-carbon chemicals in electrochemical CO2RR attributing to effective C-C coupling reactions. Regarding the different selectivity of multi-carbon chemicals but extensively applied copper-based catalysts, we classify and summarize various Cu-based catalysts through separating diverse multi-carbon products, where the modification of spatial and electronic structures is beneficial to increase the coverage of CO or lower the activation energy barrier for forming C-C bond to form the key intermediates and increase the production of multi-carbon products. Challenges and prospects involving the fundamental and development of copper-based catalysts in electrochemical CO2 reduction reaction are also proposed

Case Report: Camrelizumab combined with gemcitabine and oxaliplatin in the treatment of advanced intrahepatic cholangiocarcinoma: a case report and literature review

Intrahepatic cholangiocarcinoma (ICC) is one of the most common invasive malignant tumors, with a 5-year survival rate of less than 5%. Currently, radical surgical resection is the preferred treatment for ICC. However, most patients are only diagnosed at an advanced stage and are therefore not eligible for surgery. Herein, we present a case of advanced ICC in which radical surgery was not possible due to tumor invasion of the second porta hepatis and right hepatic artery. Six treatment cycles with a gemcitabine and oxaliplatin (GEMOX) regimen combined with camrelizumab immunotherapy achieved a partial response and successful tumor conversion, as tumor invasion of the second porta hepatis and right hepatic artery was no longer evident. The patient subsequently underwent successful radical surgical resection, including hepatectomy, caudate lobe resection, and cholecystectomy combined with lymph node dissection. Cases of patients with advanced ICC undergoing surgical resection after combined immunotherapy and chemotherapy are rare. The GEMOX regimen combined with camrelizumab demonstrated favorable antitumor efficacy and safety, suggesting that it might be a potential feasible and safe conversion therapy strategy for patients with advanced ICC

Construction of a eukaryotic expression vector for pEGFP-FST and its biological activity in duck myoblasts

Background: Follistatin (FST), a secreted glycoprotein, is intrinsically linked to muscle hypertrophy. To explore the function of duck FST in myoblast proliferation and differentiation, the pEGFP-FST eukaryotic expression vector was constructed and identified. The biological activities of this vector were analyzed by transfecting pEGFP-FST into cultured duck myoblasts using Lipofectamine\u2122 2000 and subsequently determining the mRNA expression profiles of FST and myostatin (MSTN). Results: The duck pEGFP-FST vector was successfully constructed and was confirmed to have high liposome-mediated transfection efficiency in duck myoblasts. Additionally, myoblasts transfected with pEGFP-FST had a higher biological activity. Significantly, the overexpression of FST in these cells significantly inhibited the mRNA expression of MSTN (a target gene that is negatively regulated by FST). Conclusions: The duck pEGFP-FST vector has been constructed successfully and exhibits biological activity by promoting myoblast proliferation and differentiation in vitro