A synthetic biology approach for identifying de-SUMOylation enzymes of substrates.

A synthetic biology approach using a robust reconstitution system in Escherichia coli enables the identification of plant ubiquitin-like proteases responsible for removing the small ubiquitin-like modifier (SUMO) post-translational modifications from specific protein substrates.

Identifying Risk Factors for Urinary Tract Stones in Pregnant Women at a Tertiary Hospital in China: A Retrospective Survey.

Background: Urinary tract stones present significant health risks to pregnant women and their foetuses. However, the specific risk factors contributing to stone formation during pregnancy, particularly within the Chinese population, remain poorly understood. This retrospective survey aimed to identify demographic, clinical, and obstetric risk factors associated with urinary tract stones in pregnant women at a tertiary hospital in China.

Network structures and mitigation potential of trade linked global agricultural greenhouse gas emissions.

Since agriculture is a major source of greenhouse gas emissions, accurately calculating these emissions is essential for simultaneously addressing climate change and food security challenges. This paper explores the critical role of trade in transferring agricultural greenhouse gas (AGHG) emissions throughout global agricultural supply chains. We develop a detailed AGHG emission inventory with comprehensive coverage across a wide range of countries and emission sources at first.

Imaginary-Connected Embedding in Complex Space for Unseen Attribute-Object Discrimination.

Compositional Zero-Shot Learning (CZSL) aims to recognize novel compositions of seen primitives. Prior studies have attempted to either learn primitives individually (non-connected) or establish dependencies among them in the composition (fully-connected). In contrast, human comprehension of composition diverges from the aforementioned methods as humans possess the ability to make composition-aware adaptation for these primitives, instead of inferring them rigidly through the aforementioned methods.

Pseudo-Labeling Based Practical Semi-Supervised Meta-Training for Few-Shot Learning.

Most existing few-shot learning (FSL) methods require a large amount of labeled data in meta-training, which is a major limit. To reduce the requirement of labels, a semi-supervised meta-training (SSMT) setting has been proposed for FSL, which includes only a few labeled samples and numbers of unlabeled samples in base classes. However, existing methods under this setting require class-aware sample selection from the unlabeled set, which violates the assumption of unlabeled set.

Differential Contributions of Fibroblast Subpopulations to Intercellular Communication in Eosinophilic Esophagitis.

Fibroblast heterogeneity remains undefined in eosinophilic esophagitis (EoE), an allergic inflammatory disorder complicated by fibrosis. We utilized publicly available single-cell RNA sequencing data (GSE201153) of EoE esophageal biopsies to identify fibroblast sub-populations, related transcriptomes, disease status-specific pathways and cell-cell interactions. IL13-treated fibroblast cultures were used to model active disease.

Influence of Heat Treatment Condition on the Microstructure, Microhardness and Corrosion Resistance of Ag-Sn-In-Ni-Te Alloy Wire.

Ag-Sn-In-Ni-Te alloy ingots were produced through a heating-cooling combined mold continuous casting technique; they were then drawn into wires. However, during the drawing process, the alloy wires tended to harden, making further diameter reduction challenging. To overcome this, heat treatment was necessary to soften the previously drawn wires.

A Survey of Label-Efficient Deep Learning for 3D Point Clouds.

In the past decade, deep neural networks have achieved significant progress in point cloud learning. However, collecting large-scale precisely-annotated point clouds is extremely laborious and expensive, which hinders the scalability of existing point cloud datasets and poses a bottleneck for efficient exploration of point cloud data in various tasks and applications. Label-efficient learning offers a promising solution by enabling effective deep network training with much-reduced annotation efforts.

ECEA: Extensible Co-Existing Attention for Few-Shot Object Detection.

Few-shot object detection (FSOD) identifies objects from extremely few annotated samples. Most existing FSOD methods, recently, apply the two-stage learning paradigm, which transfers the knowledge learned from abundant base classes to assist the few-shot detectors by learning the global features. However, such existing FSOD approaches seldom consider the localization of objects from local to global.

Effect of Chemical Composition on the Thermoplastic Formability and Nanoindentation of Ti-Based Bulk Metallic Glasses.

A series of TiZrBeNi ( = 4, 6, 8, 10 at.%) and TiZrBeCu ( = 4, 6, 8 at.%) bulk metallic glasses were investigated to examine the influence of Ni and Cu content on the viscosity, thermoplastic formability, and nanoindentation of Ti-based bulk metallic glasses.

Latent Semantic Consensus for Deterministic Geometric Model Fitting.

Estimating reliable geometric model parameters from the data with severe outliers is a fundamental and important task in computer vision. This paper attempts to sample high-quality subsets and select model instances to estimate parameters in the multi-structural data. To address this, we propose an effective method called Latent Semantic Consensus (LSC).

Weakly-Supervised RGBD Video Object Segmentation.

Depth information opens up new opportunities for video object segmentation (VOS) to be more accurate and robust in complex scenes. However, the RGBD VOS task is largely unexplored due to the expensive collection of RGBD data and time-consuming annotation of segmentation. In this work, we first introduce a new benchmark for RGBD VOS, named DepthVOS, which contains 350 videos (over 55k frames in total) annotated with masks and bounding boxes.

Adaptive Relation-Aware Network for zero-shot classification.

Supervised learning-based image classification in computer vision relies on visual samples containing a large amount of labeled information. Considering that it is labor-intensive to collect and label images and construct datasets manually, Zero-Shot Learning (ZSL) achieves knowledge transfer from seen categories to unseen categories by mining auxiliary information, which reduces the dependence on labeled image samples and is one of the current research hotspots in computer vision. However, most ZSL methods fail to properly measure the relationships between classes, or do not consider the differences and similarities between classes at all.

Demonstration of real-time positron emission tomography biology-guided radiotherapy delivery to targets.

Background: Biology-guided radiotherapy (BgRT) is a novel technology that uses positron emission tomography (PET) data to direct radiotherapy delivery in real-time. BgRT enables the precise delivery of radiation doses based on the PET signals emanating from PET-avid tumors on the fly. In this way, BgRT uniquely utilizes radiotracer uptake as a biological beacon for controlling and adjusting dose delivery in real-time to account for target motion.

Exploring Separable Attention for Multi-Contrast MR Image Super-Resolution.

Super-resolving the magnetic resonance (MR) image of a target contrast under the guidance of the corresponding auxiliary contrast, which provides additional anatomical information, is a new and effective solution for fast MR imaging. However, current multi-contrast super-resolution (SR) methods tend to concatenate different contrasts directly, ignoring their relationships in different clues, e.g.

Few-Shot Medical Image Segmentation via Generating Multiple Representative Descriptors.

Automatic medical image segmentation has witnessed significant development with the success of large models on massive datasets. However, acquiring and annotating vast medical image datasets often proves to be impractical due to the time consumption, specialized expertise requirements, and compliance with patient privacy standards, etc. As a result, Few-shot Medical Image Segmentation (FSMIS) has become an increasingly compelling research direction.

Persistence of antibodies 5 years after hepatitis B vaccination in preterm birth children: A retrospective cohort study using real-world data.

Previous studies did not provide substantial evidence for long-term immune persistence after the hepatitis B vaccine (HepB) in preterm birth (PTB) children. Consequently, there is ongoing controversy surrounding the booster immunization strategy for these children. Therefore, we conducted a retrospective cohort study to evaluate the disparities in immune persistence between PTB children and full-term children.

Graph Transformer GANs With Graph Masked Modeling for Architectural Layout Generation.

We present a novel graph Transformer generative adversarial network (GTGAN) to learn effective graph node relations in an end-to-end fashion for challenging graph-constrained architectural layout generation tasks. The proposed graph-Transformer-based generator includes a novel graph Transformer encoder that combines graph convolutions and self-attentions in a Transformer to model both local and global interactions across connected and non-connected graph nodes. Specifically, the proposed connected node attention (CNA) and non-connected node attention (NNA) aim to capture the global relations across connected nodes and non-connected nodes in the input graph, respectively.

Unsupervised spatially embedded deep representation of spatial transcriptomics.

Optimal integration of transcriptomics data and associated spatial information is essential towards fully exploiting spatial transcriptomics to dissect tissue heterogeneity and map out inter-cellular communications. We present SEDR, which uses a deep autoencoder coupled with a masked self-supervised learning mechanism to construct a low-dimensional latent representation of gene expression, which is then simultaneously embedded with the corresponding spatial information through a variational graph autoencoder. SEDR achieved higher clustering performance on manually annotated 10 × Visium datasets and better scalability on high-resolution spatial transcriptomics datasets than existing methods.

The Importance of Expert Knowledge for Automatic Modulation Open Set Recognition.

Automatic modulation classification (AMC) is an important technology for the monitoring, management, and control of communication systems. In recent years, machine learning approaches are becoming popular to improve the effectiveness of AMC for radio signals. However, the automatic modulation open-set recognition (AMOSR) scheme that aims to identify the known modulation types and recognize the unknown modulation signals is not well studied.

SipMaskv2: Enhanced Fast Image and Video Instance Segmentation.

We propose a fast single-stage method for both image and video instance segmentation, called SipMask, that preserves the instance spatial information by performing multiple sub-region mask predictions. The main module in our method is a light-weight spatial preservation (SP) module that generates a separate set of spatial coefficients for the sub-regions within a bounding-box, enabling a better delineation of spatially adjacent instances. To better correlate mask prediction with object detection, we further propose a mask alignment weighting loss and a feature alignment scheme.

A Bayesian Federated Learning Framework With Online Laplace Approximation.

Federated learning (FL) allows multiple clients to collaboratively learn a globally shared model through cycles of model aggregation and local model training, without the need to share data. Most existing FL methods train local models separately on different clients, and then simply average their parameters to obtain a centralized model on the server side. However, these approaches generally suffer from large aggregation errors and severe local forgetting, which are particularly bad in heterogeneous data settings.

Fish Rhbdd3 positively regulates IFN response through RIG-I signaling pathway.

Rhomboid domain-containing protein 3 (Rhbdd3) is a member of the rhomboid family, which can modulate the innate immune response in mammals. Nonetheless, the function and regulatory mechanism of fish Rhbdd3 during viral infection have not been characterized. In this study, Rhbdd3 was firstly cloned from common carp (Cyprinus carpio) and nominated as CcRhbdd3.

SRRM4-mediated REST to REST4 dysregulation promotes tumor growth and neural adaptation in breast cancer leading to brain metastasis.

Background: Effective control of brain metastasis remains an urgent clinical need due a limited understanding of the mechanisms driving it. Although the gain of neuro-adaptive attributes in breast-to-brain metastases (BBMs) has been described, the mechanisms that govern this neural acclimation and the resulting brain metastasis competency are poorly understood. Herein, we define the role of neural-specific splicing factor Serine/Arginine Repetitive Matrix Protein 4 (SRRM4) in regulating microenvironmental adaptation and brain metastasis colonization in breast cancer cells.